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The article delves into enhancing Relevance Feedback (RF) using brain signals alongside pseudo-relevance and implicit signals. Topics include the challenges of RF using traditional methods, the integration of brain signals to improve RF accuracy, and the framework's effectiveness in diverse search scenarios.

The proper functioning of judicial and legislative institutions requires the efficient retrieval of legal documents from extensive datasets. Legal Information Retrieval focuses on investigating how to efficiently handle these datasets, enabling the retrieval of pertinent information from them. Relevance Feedback, an important aspect of Information Retrieval systems, utilizes the relevance information provided by the user to enhance document retrieval for a specific request. However, there is a lack of available corpora containing this information, particularly for the legislative scenario. Thus, this paper presents Ulysses-RFCorpus, a Relevance Feedback corpus for legislative information retrieval, built in the real-case scenario of the Brazilian Chamber of Deputies. To the best of our knowledge, this corpus is the first publicly available of its kind for the Brazilian Portuguese language. It is also the only corpus that contains feedback information for legislative documents, as the other corpora found in the literature primarily focus on judicial texts. We also used the corpus to evaluate the performance of the Brazilian Chamber of Deputies’ Information Retrieval system. Thereby, we highlighted the model’s strong performance and emphasized the dataset’s significance in the field of Legal Information Retrieval. [ABSTRACT FROM AUTHOR]

Currently, most of the CBIR systems are based on the centralized computing model. Some are stand-alone applications while others are web-based systems. A centralized system maintains central nodes to handle the query requests. It keeps the entire feature descriptor database in a centralized server. Upon retrieving the relevant images according to feature similarity measures, the content will be transferred directly from the content server to the requesting host. The drawback of the centralized system is its limited scalability for handling growing volumes of retrieval requests and large image databases. The worldwide infrastructure of computers and networks created an exciting opportunity for collecting vast amounts of data and for sharing computers and resources on an unprecedented scale. In the last few years, the emerging Peer-to Peer (P2P) model has become a very powerful and attractive paradigm for developing Internet-scale file systems and sharing resources. This paper explores the various approaches and efforts for design of content-based image retrieval in P2P Environment. [ABSTRACT FROM AUTHOR]

Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback. PRF is used to avoid the limitations of the traditional RF approach, which is a human-in-the-loop process. Although the pseudo-relevance feedback set contains noise, PRF can perform retrieval reasonably effectively. For implementing PRF, the Rocchio algorithm has been considered reasonably effective and is a well-established baseline method. However, it simply treats all of the top k feedback images as being equally similar to the query. Therefore, we present a block-based PRF approach for improving image retrieval performance. In this approach, images in the positive and negative feedback sets are further divided into predefined blocks, each of which contains one to several images, and blocks containing higher- or lower-ranked images will be assigned higher or lower weights, respectively. Experiments using the NUS-WIDE-LITE and Caltech 256 datasets and two different feature representations consistently show that the proposed approach using 30 blocks outperforms the baseline PRF in terms of P@10, P@20, and P@50. Furthermore, we show that a system that incorporates the user's feedback allows the 30-block-based PRF approach to perform even better. [ABSTRACT FROM AUTHOR]

Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches. [ABSTRACT FROM AUTHOR]

It is a challenging task to recommend a peer to a user based on the user’s requirement. Users may have expertise in multiple sub-domains, due to which peer recommendation is a nontrivial task. In this paper, we model peers as nodes in a graph and perform a community search. Weighted attributes are associated with every node in the graph. We propose two novel methods to compute the weights of the attributes. Relevance feedback is a popular technique used to improve the performance of retrieval systems. We propose to use negative relevance feedback in an attributed graph for peer recommendation. We use CL-tree for indexing the nodes in the graph. We compare the proposed system with the state-of-the-art on standard datasets, and our system outperforms the rival system. [ABSTRACT FROM AUTHOR]

Scholars routinely search relevant papers to discover and put a new idea into proper context. Despite ongoing advances in scholarly retrieval technologies, locating relevant papers through keyword queries is still quite challenging due to the massive expansion in the size of the research paper repository. To tackle this problem, we propose a novel real-time feedback query expansion technique, which is a two-stage interactive scholarly search process. Upon receiving the initial search query, the retrieval system provides a ranked list of results. In the second stage, a user selects a few relevant papers, from which useful terms are extracted for query expansion. The newly expanded query is run against the index in real time to generate the final list of research papers. In both stages, citation analysis is involved in further improving the quality of the results. The novelty of the approach lies in the combined exploitation of query expansion and citation analysis that may bring the most relevant papers to the top of the search results list. The experimental results on the Association of Computational Linguistics (ACL) Anthology Network data set demonstrate that this technique is effective and robust for locating relevant papers regarding normalised discounted cumulative gain (nDCG), precision and recall rates than several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cloud servers are highly prone to resource bottleneck failures. In this work, we propose an ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck identification. The workload at cloud servers is highly dynamic in nature. To support continuous online learning of resource bottleneck identification models, we propose relevance feedback based online learning of proposed ensemble model. Here we propose to analyse, catastrophe forgetting and incremental architectural evolution as two fundamental challenges associated with online adaptation of LSTM-based multiclass classifier models. To avoid catastrophic forgetting, we propose a combination of distillation loss and the standard crossentropy loss. For architectural evolution, we propose and analyse three different alternatives to update the architecture of the bottleneck identification model on the fly. We evaluate the proposed approaches on a real world dataset collected in an industrial case study and on a dataset collected in a virtual environment setup using Docker containers. The experimental results show that the proposed approaches outperform existing state-of-the-art methods for bottleneck identification. [ABSTRACT FROM AUTHOR]

The core of a content-based image retrieval (CBIR) system is based on an effective understanding of the visual contents of images due to which a CBIR system can be termed as accurate. One of the most prominent issues which affect the performance of a CBIR system is the semantic gap. It is a variance that exists between low-level patterns of an image and high-level abstractions as perceived by humans. A robust image visual representation and relevance feedback (RF) can bridge this gap by extracting distinctive local and global features from the image and by incorporating valuable information stored as feedback. To handle this issue, this article presents a novel adaptive complementary visual word integration method for a robust representation of the salient objects of the image using local and global features based on the bag-of-visual-words (BoVW) model. To analyze the performance of the proposed method, three integration methods based on the BoVW model are proposed in this article: (a) integration of complementary features before clustering (called as non-adaptive complementary feature integration), (b) integration of non-adaptive complementary features after clustering (called as a non-adaptive complementary visual words integration), and (c) integration of adaptive complementary feature weighting after clustering based on self-paced learning (called as a proposed method based on adaptive complementary visual words integration). The performance of the proposed method is further enhanced by incorporating a log-based RF (LRF) method in the proposed model. The qualitative and quantitative analysis of the proposed method is carried on four image datasets, which show that the proposed adaptive complementary visual words integration method outperforms as compared with the non-adaptive complementary feature integration, non-adaptive complementary visual words integration, and state-of-the-art CBIR methods in terms of performance evaluation metrics. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach to interactively propagate annotations representing the historians' knowledge on a database of lettrine images manually populated by historians (with annotations). Based on a novel document indexing processing scheme which combines the use of the Zipf law and the use of bag of patterns, our approach extends the bag-of-words model to represent the knowledge by visual features through relevance feedback. Then, annotation propagation is automatically performed to propagate knowledge to the lettrine database. Our approach is presented together with preliminary experimental results and an illustrative example. [ABSTRACT FROM AUTHOR]

In this Letter, the authors deal with ranking of keyword‐combined searches in relational databases based on relevance to the user query, which they call KEYSIM searches. They formally define KEYSIM searches and propose a threshold‐based method for efficiently processing KEYSIM searches. Their proposed method is the first one to find top‐k results considering both numerical similarity and textual similarity. Through the experiments using five real and synthetic data sets, they show the efficiency and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer with isolated sentences, as opposed to full documents, for relevance feedback. Under the weak assumption that more time and effort is required to review an entire document than a single sentence, simulation results indicate that the use of isolated sentences for relevance feedback can yield comparable accuracy and higher efficiency, relative to the state-of-the-art baseline model implementation (BMI) of the AutoTAR continuous active learning ("CAL") method employed in the TREC 2015 and 2016 Total Recall Track. [ABSTRACT FROM AUTHOR]

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy. [ABSTRACT FROM AUTHOR]

Over the years, many content-based image retrieval (CBIR) methods, which use SVM-based relevant feedback, are proposed to improve the performance of image retrieval systems. However, the performance of these methods is low due to the following limitations: (1) ignore the unlabeled samples; (2) only exploit the global Euclidean structure and (3) not taking advantage of the various useful aspects of the object. In order to solve the first problem, we propose a graph-based semisupervised learning (GSEL), which can add positive samples and construct balanced sets. With the second problem, we propose a manifold learning for dimensional reduction (MAL), which exploits the geometric properties of the manifold data. With the third problem, we propose a combination of classifiers by aspect (CCA), which exploits the various useful aspects of the object. Experimental results reported in the Corel Photo Gallery (with 31,695 images), which demonstrate the accuracy of our proposed method in improving the performance of the content-based image retrieval system. [ABSTRACT FROM AUTHOR]

Highlights • Innovative methods for ranking résumés or curriculum vitae automatically • Methods based on the similarity between résumés instead of résumés and a job offer • Free of external resources such as word embedding and ontologies • Experiments done over a large corpus of real recruitment and selection processes • In average, 93% of résumés are ranked correctly. Abstract With the success of the electronic recruitment, now it is easier to find a job offer and apply for it. However, due to this same success, nowadays, human resource managers tend to receive high volumes of applications for each job offer. These applications turn into large quantities of documents, known as résumés or curricula vitae, that need to be processed quickly and correctly. To reduce the time necessary to process the résumés, human resource managers have been working with the scientific community to create systems that automate their ranking. Until today, most of these systems are based on the comparison of job offers and résumés. Nevertheless, this comparison is impossible to do in data sets where job offers are no longer available, as it happens in this work. We present two methods to rank résumés that do not use job offers or any semantic resource, unlike existing state-of-the-art systems. The methods are based on what we call Inter-Résumé Proximity , which is the lexical similarity between only résumés sent by candidates in response to the same job offer. Besides, we propose the use of Relevance Feedback, at general and lexical levels to improve the ranking of résumés. Relevance Feedback is applied using techniques based on similarity coefficients and vocabulary scoring. All the methods have been tested on a large corpus of 171 real selection processes, which correspond to more than 14,000 résumés. The developed methods can rank correctly, in average, 93% of the résumés sent to each job posting. The outcomes presented here show that it is not necessary to use job offers or semantic resources to provide high quality results. Furthermore, we observed that résumés have particular characteristics that as ensemble, work as a facial composite and provide more information about the job posting than the job offer. This certainly will change how systems analyze and rank résumés. [ABSTRACT FROM AUTHOR]

Highlights • An active learning-based tool supporting researchers finding relevant paper faster. • Use of domain knowledge to faster find the first relevant paper deterministically. • An accurate estimator for the total number of relevant papers. • With the estimator, the system can stop at target recall with little overhead. • An error correction strategy efficiently resolves human errors. Abstract Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. Finding relevant papers can be hard due to the huge amount of candidates provided by search. FAST2 is a novel tool for assisting the researchers to find the next promising paper to read. This paper describes FAST2 and tests it on four large systematic literature review datasets. We show that FAST2 robustly optimizes the human effort to find most (95%) of the relevant software engineering papers while also compensating for the errors made by humans during the review process. The effectiveness of FAST2 can be attributed to three key innovations: (1) a novel way of applying external domain knowledge (a simple two or three keyword search) to guide the initial selection of papers—which helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be found—which helps the reviewer decide when to stop the review; (3) a novel human error correction algorithm—which corrects a majority of human misclassifications (labeling relevant papers as non-relevant or vice versa) without imposing too much extra human effort. [ABSTRACT FROM AUTHOR]

Abstract User interactions are examples of sources of evidence that can be adopted by Information Retrieval systems to predict the documents relevant to user information needs. This work is focused on the problem of uniformly modelling heterogeneous forms of user interaction that are selected as sources for feedback. The problem of uniform source modelling is addressed by way of a complete methodology. The methodology aims at designing, implementing and evaluating a system that validates an experimental hypothesis. The hypothesis being validated regards the possible factors that can explain the user perception of relevance through the evidence gathered from the user interaction. The objective is to obtain and exploit a representation of the factors in the role of a new information need representation. The methodology aims at being general and not tailored to a specific source. The methodology defines the set of steps needed for obtaining a vector subspace-based representation of the hypotheses to further exploit this representation for document re-ranking. The objective of this paper is to present the methodology, consider previous works in terms of the proposed methodology and make the connection with other methodologies and frameworks explicit. [ABSTRACT FROM AUTHOR]

We present a relevance feedback based browsing methodology using different representations for time series data. The outperforming representation type, e.g., among dual-tree complex wavelet transformation, Fourier, symbolic aggregate approximation (SAX), is learned based on user annotations of the presented query results with representation feedback. We present the use of autoencoder type neural networks to summarize time series or its representations into sparse vectors, which serves as another representation learned from the data. Experiments on 85 real data sets confirm that diversity in the result set increases precision, representation feedback incorporates item diversity and helps to identify the appropriate representation. The results also illustrate that the autoencoders can enhance the base representations, and achieve comparably accurate results with reduced data sizes. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge about insufficiency of training set and limited feedback information in each relevance feedback (RF) round during the process of content based image retrieval (CBIR). We propose a novel active learning scheme to utilize the labeled and unlabeled images to build the initial Support Vector Machine (SVM) classifier for image retrieving. In our framework, two main components, a pseudo-label strategy and an improved active learning selection method, are included. Moreover, a feature subspace partition algorithm is proposed to model the retrieval target from users by the analysis from relevance labeled images. Experimental results demonstrate the superiority of the proposed method on a range of databases with respect to the retrieval accuracy. [ABSTRACT FROM AUTHOR]

Person re-identification is a challenging cross-camera matching problem, which is inherently subject to domain shift. To mitigate it, many solutions have been proposed so far, based on four kinds of approaches: supervised and unsupervised domain adaptation, direct transfer, and domain generalisation; in particular, the first two approaches require target data during system design , respectively labelled and unlabelled. In this work, we consider a very different approach, known as human-in-the-loop HITL), which consists of exploiting user's feedback on target data processed during system operation to improve re-identification accuracy. Although it seems particularly suited to this application, given the inherent interaction with a human operator, HITL methods have been proposed for person re-identification by only a few works so far, and with a different purpose than addressing domain shift. However, we argue that HITL deserves further consideration in person re-identification, also as a potential alternative solution against domain shift. To substantiate our view, we consider simple HITL implementations which do not require model re-training or fine-tuning: they are based on well-known relevance feedback algorithms for content-based image retrieval, and of novel versions of them we devise specifically for person re-identification. We then conduct an extensive, cross-data set experimental evaluation of our HITL implementations on benchmark data sets, and compare them with a large set of existing methods against domain shift, belonging to the four categories mentioned above. Our results provide evidence that HITL can be as effective as, or even outperform, existing ad hoc solutions against domain shift for person re-identification, even under the simple implementations we consider. We believe that these results can foster further research on HITL in the person re-identification field, where, in our opinion, its potential has not been thoroughly explored so far. • We focus on relevance feedback algorithms to implement human-in-the-loop. • We revisit existing RF algorithms and adapt them to person re-identification. • We show that HITL-RF is an effective and efficient online domain adaptation solution. [ABSTRACT FROM AUTHOR]

Word embedding models are able to accurately model the semantic content of words. The process of extracting a set of word embedding vectors from a text document is similar to the feature extraction step of the Bag-of-Features (BoF) model, which is usually used in computer vision tasks. This gives rise to the proposed Bag-of-Embedded Words (BoEW) model that can efficiently represent text documents overcoming the limitations of previously predominantly used techniques, such as the textual Bag-of-Words model. The proposed method extends the regular BoF model by a) incorporating a weighting mask that allows for altering the importance of each learned codeword and b) by optimizing the model end-to-end (from the word embeddings to the weighting mask). Furthermore, the BoEW model also provides a fast way to fine-tune the learned representation towards the information need of the user using relevance feedback techniques. Finally, a novel spherical entropy objective function is proposed to optimize the learned representation for retrieval using the cosine similarity metric. [ABSTRACT FROM AUTHOR]

Most of existing approaches in keyword spotting are system-oriented, which did not take into consideration the user's needs. However, a user may want to find words, sentences, or texts that match his target image in his mind. The challenge here is how to formulate one's mental image to reach what he is looking for. The key idea is to design and build a model that properly adapts the human reasoning in information searching through an interactive process. We propose a mental model for handwritten keyword spotting based on relevance feedback, feature weighting, and optimization. This model meets simultaneously the user's needs, the system behavior, and the user -- system relationship. In an appropriate feature space, the query is progressively built from user-supplied keywords, old queries, and spotted images. This dynamic process not only converges toward the desired word images, but also helps the hesitant user to clarify progressively what he is looking for. The proposed model was showcased via a user-friendly interface, which we tested including real users on three well-known handwritten datasets; Institute for Communications, Braunschweig University, Germany/École Nationale d'Ingénieurs de Tunis, Tunisia, Institut für informatik und Angewandte Mathematik, and George Washington. The experimental results show that the proposed method provides promising scores with a reasonable number of refinements. [ABSTRACT FROM AUTHOR]

Offline evaluation of information retrieval systems depends on test collections. These datasets provide the researchers with a corpus of documents, topics and relevance judgements indicating which documents are relevant for each topic. Gathering the latter is costly, requiring human assessors to judge the documents. Therefore, experts usually judge only a portion of the corpus. The most common approach for selecting that subset is pooling. By intelligently choosing which documents to assess, it is possible to optimise the number of positive labels for a given budget. For this reason, much work has focused on developing techniques to better select which documents from the corpus merit human assessments. In this article, we propose using relevance feedback to prioritise the documents when building new pooled test collections. We explore several state-of-the-art statistical feedback methods for prioritising the documents the algorithm presents to the assessors. A thorough comparison on eight Text Retrieval Conference (TREC) datasets against strong baselines shows that, among other results, our proposals improve in retrieving relevant documents with lower assessment effort than other state-of-the-art adjudicating methods without harming the reliability, fairness and reusability. [ABSTRACT FROM AUTHOR]

In interactive retrieval tasks, one of the main objectives is to maximize the user information gain throughout search sessions. Retrieving many relevant items is quite important, but it does not necessarily completely satisfy the user needs. When only relevant near-duplicate items are retrieved, the amount of different concepts users are able to extract from the target collection is very limited. Therefore, broadening the number of concepts present in a result set may improve the overall search experience. Diversifying concepts present in the retrieved set is one possibility for increasing the information gain in a single search iteration, maximizing the likelihood of including at least some relevant items for each possible intent of ambiguous or underspecified queries. Relevance feedback approaches may also take advantage of diverse results to improve internal machine learning models. In this context, this work proposes and analyses several multimodal image retrieval approaches built over a learning framework for relevance feedback on diversified results. Our experimental analysis shows that different retrieval modalities are positively impacted by diversity, but achieve best retrieval effectiveness with diversification applied at different moments of a search session. Moreover, the best results are achieved with a query-by-example approach using multimodal information obtained from feedback. In summary, we demonstrate that learning with diversity is an effective alternative for boosting multimodal interactive learning approaches. [ABSTRACT FROM AUTHOR]

Efficient query expansion (QE) terms selection methods are really very important for improving the accuracy and efficiency of the system by removing the irrelevant and redundant terms from the top-retrieved feedback documents corpus with respect to a user query. Each individual QE term selection method has its weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, we present a new method for QE based on fuzzy logic considering the top-retrieved document as relevance feedback documents for mining additional QE terms. Different QE terms selection methods calculate the degrees of importance of all unique terms of top-retrieved documents collection for mining additional expansion terms. These methods give different relevance scores for each term. The proposed method combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. All the experiments are performed on TREC and FIRE benchmark datasets. The proposed QE method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a significant higher average recall rate, average precision rate and F measure on both datasets. [ABSTRACT FROM AUTHOR]

Vast collections of documents available in image format need to be indexed for information retrieval purposes. In this framework, word spotting is an alternative solution to optical character recognition (OCR), which is rather inefficient for recognizing text of degraded quality and unknown fonts usually appearing in printed text, or writing style variations in handwritten documents. Over the past decade there has been a growing interest in addressing document indexing using word spotting which is reflected by the continuously increasing number of approaches. However, there exist very few comprehensive studies which analyze the various aspects of a word spotting system. This work aims to review the recent approaches as well as fill the gaps in several topics with respect to the related works. The nature of texts and inherent challenges addressed by word spotting methods are thoroughly examined. After presenting the core steps which compose a word spotting system, we investigate the use of retrieval enhancement techniques based on relevance feedback which improve the retrieved results. Finally, we present the datasets which are widely used for word spotting, we describe the evaluation standards and measures applied for performance assessment and discuss the results achieved by the state of the art. [ABSTRACT FROM AUTHOR]

Background and Objective Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the “semantic gap” and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. Methods We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel “similarity positional score” mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Results Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2–3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Conclusions Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the “semantic gap” problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field. [ABSTRACT FROM AUTHOR]

We propose a texture insensitive, region based image saliency detection algorithm, having excellent detection and localization properties, to obtain salient objects. We use a total variation based regularizer to suppress textures from the image and to make the method invariant to textural variations in the scene. This leads to an image that contains piecewise constant gray valued regions. This texture-free image is sparsely segmented into a small number of regions using the expectation maximization algorithm assuming a Gaussian mixture model. We compute three different saliency measures for every region using its intensity and spatial features. We adopt a relevance feedback mechanism to obtain weights for combining the three saliency measures and obtain the final saliency map. Next we input the thresholded saliency map to an image matting technique and extract the salient objects from the image with exact boundaries. Experimental comparisons with existing saliency detection algorithms demonstrate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

In many research and application areas, such as information retrieval and machine learning, we often encounter dealing with a probability distribution that is mixed by one distribution that is relevant to our task in hand and the other that is irrelevant and that we want to get rid of. Thus, it is an essential problem to separate the irrelevant distribution from the mixture distribution. This article is focused on the application in Information Retrieval, where relevance feedback is a widely used technique to build a refined query model based on a set of feedback documents. However, in practice, the relevance feedback set, even provided by users explicitly or implicitly, is often a mixture of relevant and irrelevant documents. Consequently, the resultant query model (typically a term distribution) is often a mixture rather than a true relevance term distribution, leading to a negative impact on the retrieval performance. To tackle this problem, we recently proposed a Distribution Separation Method (DSM), which aims to approximate the true relevance distribution by separating a seed irrelevance distribution from the mixture one. While it achieved a promising performance in an empirical evaluation with simulated explicit irrelevance feedback data, it has not been deployed in the scenario where one should automatically obtain the irrelevance feedback data. In this article, we propose a substantial extension of the basic DSM from two perspectives: developing a further regularization framework and deploying DSM in the automatic irrelevance feedback scenario. Specifically, in order to avoid the output distribution of DSM drifting away from the true relevance distribution when the quality of seed irrelevant distribution (as the input to DSM) is not guaranteed, we propose a DSM regularization framework to constrain the estimation for the relevance distribution. This regularization framework includes three algorithms, each corresponding to a regularization strategy incorporated in the objective function of DSM. In addition, we exploit DSM in automatic (i.e., pseudo) irrelevance feedback, by automatically detecting the seed irrelevant documents via three different document reranking methods. We have carried out extensive experiments based on various TREC datasets, in order to systematically evaluate the proposed methods. The experimental results demonstrate the effectiveness of our proposed approaches in comparison with various strong baselines. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) is a hot research topic in computer vision. Relevance Feedback (RF) is a powerful technique that can help to increase quality of the CBIR. In this study, a new method for addressing the problem of the CBIR is proposed. To this end, a novel Convolutional Neural Network (CNN) and a new framework of applying RF have been investigated. Also, in order to obtain more efficient image features and reduce the dimensionality of feature vectors, we applied Generalized Discriminant Analysis (GDA) on the extracted features. The proposed method was tested on three benchmark datasets including the OT, Corel-1000 and Caltech-101. The experimental results illustrate that the proposed method performs better compared to the state-of-the-art in the field. Our approach achieved remarkable performances in image retrieval by demonstrating mean Average Precisions (mAPs) of 96.8%, 95.62% and 98.78% on the OT, Corel-1000 and Caltech-101, respectively. [ABSTRACT FROM AUTHOR]

Purpose Conventional studies mainly classify a term’s appearance in the retrieved documents as either relevant or irrelevant for application. The purpose of this paper is to differentiate the term’s appearances in the retrieved documents in more detailed situations to generate relevance information and demonstrate the applicability of the derived information in combination with current methods of query expansion.Design/methodology/approach A method was designed first to utilize the derived information owing to term appearance differentiation within a conventional query expansion approach that has been proven as an effective technology in the enhancement of information retrieval. Then, an information retrieval system was developed to demonstrate the realization and sustain the study of the method. Formal tests were conducted to examine the distinguishing capability of the proposed information utilized in the method.Findings The experimental results show that substantial differences in performances can be achieved between the proposed method and the conventional query expansion method alone.Practical implications Since the proposed information resides at the bottom of the information hierarchy of relevance feedback, any technology regarding the application of relevance feedback information could consider the utilization of this piece of information.Originality/value The importance of the study is the disclosure of the applicability of the proposed information beyond current usage of term appearances in relevant/irrelevant documents and the initiation of a query expansion technology in the application of this information. [ABSTRACT FROM AUTHOR]

With the amount and variety of information available on digital repositories, answering complex user needs and personalizing information access became a hard task. Putting the user in the retrieval loop has emerged as a reasonable alternative to enhance search effectiveness and consequently the user experience. Due to the great advances on machine learning techniques, optimizing search engines according to user preferences has attracted great attention from the research and industry communities. Interactively learning-to-rank has greatly evolved over the last decade but it still faces great theoretical and practical obstacles. This paper describes basic concepts and reviews state-of-the-art methods on the several research fields that complementarily support the creation of interactive information retrieval (IIR) systems. By revisiting ground concepts and gathering recent advances, this article also intends to foster new research activities on IIR by highlighting great challenges and promising directions. The aggregated knowledge provided here is intended to work as a comprehensive introduction to those interested in IIR development, while also providing important insights on the vast opportunities of novel research. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of image retrieval in large databases with a big semantic gap by a relevance feedback procedure. We present a novel algorithm for modelling the users׳s preferences in the content-based image retrieval system. The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, and estimates the parameters of several local logistic regression models whose inputs are the low-level image features. A Principal Component Analysis method is applied to the original vector to reduce its high dimensionality. The relevance probabilities predicted by these local models are combined by means of a weighted average. These weights are obtained according to the variance explained by the group of principal components used for each local model. These models are dynamically estimated in each iteration of the relevance feedback algorithm until the user is satisfied. This novel procedure has been tested in a collection with a large semantic gap, the Wikipedia collection. Two types of experiments have been performed, one with an automatic user and another with a typical user. The method is compared to some recent similar approaches in literature, obtaining very good performance in terms of the MAP evaluation measure. [ABSTRACT FROM AUTHOR]

The content-based image retrieval methods are developed to help people find what they desire based on preferred images instead of linguistic information. This paper focuses on capturing the image features representing details of the collar designs, which is important for people to choose clothing. The quality of the feature extraction methods is important for the queries. This paper presents several new methods for the collar-design feature extraction. A prototype of clothing image retrieval system based on relevance feedback approach and optimum-path forest algorithm is also developed to improve the query results and allows users to find clothing image of more preferred design. A series of experiments are conducted to test the qualities of the feature extraction methods and validate the effectiveness and efficiency of the RF-OPF prototype from multiple aspects. The evaluation scores of initial query results are used to test the qualities of the feature extraction methods. The average scores of all RF steps, the average numbers of RF iterations taken before achieving desired results and the score transition of RF iterations are used to validate the effectiveness and efficiency of the proposed RF-OPF prototype. [ABSTRACT FROM AUTHOR]

Resource scheduling has been one of the key challenges facing both academia and industry ever since the inauguration of cloud computing. Most of the existing research and practices have been focused on the maximization of the profits of cloud providers, whereas attention to the real needs of cloud users has largely been neglected. In this research, we propose a resource scheduling mechanism empowered with a relevance feedback network, which can be employed by a cloud provider to better meet a user's resource needs. Our approach is a continuous refinement process that involves three stages: resource matching, resource selection, and feedback integration, where the feedback integration stage allows the resource scheduling history of a user to be considered to update the user's resource demands and preference. The feedback information integrated in one cycle will effectively adjust the resource matching and selection in the next cycle. Incrementally, this mechanism will produce resource selections that are closer and closer to the user's real needs. Simulation results indicated that this relevance feedback scheduling mechanism is very effective in satisfying users' diverse requirements, and it also performs well in terms of the resource utilization rate from the cloud provider's perspective. [ABSTRACT FROM AUTHOR]

Background: Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. Methods: The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. Results: A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. Conclusions: With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search. [ABSTRACT FROM AUTHOR]

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The support vector machine (SVM) based RF mechanisms have been used in different fields of image retrieval, but they often treat all positive and negative feedback samples equally, which will inevitably degrade the effectiveness of SVM-based RF approaches for CBIR. In fact, positive and negative feedback samples, different positive feedback samples, and different negative feedback samples all always have distinct properties. Moreover, each feedback interaction process is usually tedious and time-consuming because of complex visual features, so if too many times of iteration of feedback are asked, users may be impatient to interact with the CBIR system. To overcome the above limitations, we propose a new SVM-based RF approach using probabilistic feature and weighted kernel function in this paper. Firstly, the probabilistic features of each image are extracted by using principal components analysis (PCA) and the adapted Gaussian mixture models (AGMM) based dimension reduction, and the similarity is computed by employing Kullback–Leibler divergence. Secondly, the positive feedback samples and negative feedback samples are marked, and all feedback samples’ weight values are computed by utilizing the samples-based Relief feature weighting. Finally, the SVM kernel function is modified dynamically according to the feedback samples’ weight values. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper presents a simple approach to improve the image retrieval accuracy in the Void-and-Cluster Block Truncation Coding compressed domain. The proposed approach directly derives an image descriptor from the Ordered Dither Block Truncation Coding (ODBTC) data stream without performing the decoding process. The Color Histogram Feature (CHF) is generated from the two ODBTC color quantizer, while the Halftoning Local Derivative Pattern (HLDP) is constructed from the ODBTC bitmap image. The similarity between two images are measured from their CHF and HLDP features. Three schemes are involved to improve the image retrieval accuracy, including the similarity weight optimization, feature reweighting, and user relevance feedback optimization. An evolutionary stochastic algorithm is exploited to optimize the similarity weight and feature weight in the nearest neighbor distance computation, as well as in the query update of relevance feedback optimization. Section 5 shows that the proposed scheme yields a promising result, and thus it can be a very effective candidate in addressing the content-based image retrieval and image classification task. [ABSTRACT FROM AUTHOR]

Focused crawlers are effective tools for applications requiring a high number of pages belonging to a specific topic. Several strategies for implementing these crawlers have been proposed in the literature, which aim to improve crawling efficiency by increasing the number of relevant pages retrieved while avoiding non-relevant pages. However, an important aspect of these crawlers has been largely overlooked: the selection of the seed pages that serve as the starting points for a crawl. In this paper, we show that the seeds can greatly influence the performance of crawlers, and propose a new framework for automatically finding seeds. We describe a system that implements this framework and show, through a detailed experimental evaluation, that by providing crawlers a seed set that is large and varied, they not only obtain higher harvest rates but also an improved topic coverage. [ABSTRACT FROM AUTHOR]

The Web information is too heterogeneous that users have difficulties to retrieve their needed information: text, image or video. Indeed, the collaborative work presents one solution proposed to solve this problem. Collaborative retrieval enables the retrieval histories' sharing between users having the same profile across multiple tools such as annotations. However the user has always problems in choosing the terms to form his query. This paper has proposed collaborative Information Retrieval Model based on Fuzzy Confidence Network. Our approach allows the detection of relevant annotations to a given evidence source. These annotations are next filtered to determine which are relevant to consider them as a new source of information that describes the document used to improve collaborative retrieval performance.We then measure the semantic relationships between terms which will be translated by the propagation of confidence. Experiments were conducted on different queries, showing very encouraging results that could reach an improvement rate. [ABSTRACT FROM AUTHOR]

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information. [ABSTRACT FROM AUTHOR]

Information Retrieval (IR) is concerned with indexing and retrieving documents including information relevant to a user's information need. Relevance Feedback (RF) is a class of effective algorithms for improving Information Retrieval (IR) and it consists of gathering further data representing the user's information need and automatically creating a new query. In this paper, we propose a class of RF algorithms inspired by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the query vector on a subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a novel Content-Based Video Retrieval approach in order to cope with the semantic gap challenge by means of latent topics. Firstly, a supervised topic model is proposed to transform the classical retrieval approach into a class discovery problem. Subsequently, a new probabilistic ranking function is deduced from that model to tackle the semantic gap between low-level features and high-level concepts. Finally, a short-term relevance feedback scheme is defined where queries can be initialised with samples from inside or outside the database. Several retrieval simulations have been carried out using three databases and seven different ranking functions to test the performance of the presented approach. Experiments revealed that the proposed ranking function is able to provide a competitive advantage within the content-based retrieval field. [ABSTRACT FROM AUTHOR]

With a single input indoor image (including sofa, tea table, etc.), a 3D scene can be reconstructed from a single image using an existing model library in three stages: image analysis, model retrieval and relevance feedback. In the image analysis stage, we obtain the object information from the input image using geometric reasoning technology combined with an image segmentation method. In the model retrieval stage, line drawings are extracted from 2D objects and 3D models by using different line rendering methods. We exploit various tokens to represent local features and then organize them together as a star-graph to show a global description. By comparing similarity among the encoded line drawings, models are retrieved from the model library. Also, for a better user experience, we add a relevance feedback stage following the retrieval stage. The Support Vector Machine method is used to conduct the feedback operation. After this stage, the retrieved models are in conformance with the image semantic. The 3D scene is then reconstructed. Experimental results show that, driven by the given model library, indoor scenes modeling from a single image could be achieved automatically and efficiently. [ABSTRACT FROM AUTHOR]

This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, some memory is incorporated in the procedure by weighting the current and previous scores. Also, a novel evaluation procedure is proposed in this work based on the empirical commutative distribution functions of the relevant and non-relevant retrieved images. Good experimental results are achieved in very different experimental setups and tested in different databases. [ABSTRACT FROM AUTHOR]

Relevance feedback (RF) is a technique popularly used to improve the effectiveness of traditional content-based image retrieval systems. However, users must provide relevant and/or irrelevant images as feedback for their queries, which is a tedious task. To alleviate this problem, pseudo relevance feedback (PRF) can be utilized. It not only automates the manual component of RF, but can also provide reasonably good retrieval performance. Specifically, it is assumed that a fraction of the top-ranked images in the initial search results are pseudo-positive. The Rocchio algorithm is a classic approach for the implementation of RF/PRF, which is based on the query vector modification discipline. The aim is to reproduce a new query vector by taking the weighted sum of the original query and the mean vectors of the relevant and irrelevant sets. Image feature representation is the key factor affecting the PRF performance. This study is the first to examine the retrieval performances of 63 different image feature descriptors ranging from 64 to 10426 dimensionalities in the context of PRF. Experimental results are obtained based on the NUS-WIDE dataset which contains 22156 Flickr images associated with 69 concepts. It is shown that the combination of color moments, edges, wavelet textures, and locality-constrained linear coding of the bag-of-words model provides the optimal feature representation, giving relatively good retrieval effectiveness and reasonably good retrieval efficiency for Rocchio based PRF. [ABSTRACT FROM AUTHOR]

Due to the diversity of the image content, different images have different focuses, image retrieval system based on single feature has a lower performance, and it cannot apply to all images, so an image retrieval method using multi-feature fusion is proposed. In this method, the color moment in RGB color space in combination with the color histogram in HSV color space is used for color feature extraction, the improved Zernike moments are used for shape feature extraction, and the gray level co-occurrence matrix is used for texture feature extraction, then combining these three features. Finally, respectively using color features, shape features, texture features as well as the fused features for image retrieval, the experimental results show that the image retrieval method based on multi-feature fusion has better retrieval performance. [ABSTRACT FROM AUTHOR]

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts. [ABSTRACT FROM AUTHOR]

The proper use of constraints improves the data clustering performance. In this paper, we propose a novel interactive image retrieval framework using constraints. First, we extract the user׳s region of interest (ROI) from queries by simple user interaction using adaptive constraints-based seed propagation (ACSP), and obtain initial retrieval results based on the ROI. Then, we improve the retrieval results by active learning from the user׳s relevance feedback using ACSP. Since ACSP is very effective in propagating the user׳s interactive information of constraints by employing a kernel learning strategy, it successfully learns the correlation between low-level image features and high-level semantics from the ROI and relevance feedbacks. Experimental results demonstrate that the proposed framework remarkably improves the image retrieval performance by ACSP-based constraint propagation in terms of both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]