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This article describes the capability of online data storage which has been enhanced by the emergence of cloud datacenter development. Distributed Hash Table (DHT) based image retrieval system using locality sensitive hash (LSH) has provided an efficient way to set up distributed Content Based Image Retrieval (CBIR) frameworks. However, with the fixed LSH function adopted, LSH and other codebook-based distributed retrieval systems are facing the problem of flexibility, and also are difficult to satisfy the user's demand. In this article, LRFMIR is proposed to introduce semantic search into DHT based CBIR system. LRFMIR is established on a DHT based network, where a flexible result truncating strategy is employed to fuse provided results by using multiple features measurements. Experiments show that LRFMIR provides a higher accuracy and recall rate than single feature employed retrieval systems, and possesses good load balancing and query efficiency performance.

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.

To support radiologists in their decision-making regarding women breast cancer diagnosis based on mammographies, this paper addresses three challenges posed by a Clinical Decision Support System (CDSS) for breast cancer screening, diagnosis, and reporting: multimodality (via textual, graphical, and two-dimensional gestural interaction), usability (via human-computer interactions methods such as knowledge elicitation interviews, scenario-focused questionnaires, multi-fidelity prototyping and user testing), and flexibility (via selected image processing techniques ensuring manual and semi-automatic annotation of breast cancer findings based on radiologists' gestures)

In this paper, the authors propose and readapt a new concept-based approach of query expansion in the context of Arabic information retrieval. The purpose is to represent the query by a set of weighted concepts in order to identify better the user's information need. Firstly, concepts are extracted from the initially retrieved documents by the Pseudo-Relevance Feedback method, and then they are integrated into a semantic weighted tree in order to detect more information contained in the related concepts connected by semantic relations to the primary concepts. The authors use the “Arabic WordNet” as a resource to extract, disambiguate concepts and build the semantic tree. Experimental results demonstrate that measure of MAP (Mean Average Precision) is about 10% of improvement using the open source Lucene as IR System on a collection formed from the Arabic BBC news.

Due to the potential value in many areas, such as e-commerce and inventory management, fabric image retrieval, which is a special case of content-based image retrieval, has recently become a research hotspot. As a major category of textile fabrics, patterned fabrics have a diverse and complex appearance, making the retrieval task more challenging. To address this situation, this paper proposes a novel approach for patterned fabric based on the non-subsampled contourlet transform (NSCT) feature descriptor and relevance feedback technique. To integrate the color information into the NSCT feature descriptor, we extract the feature of patterned fabric images in HSV color space. An outlier rejection-based parametric relevance feedback algorithm is employed to adjust the similarity matrix to improve the retrieval results. The experimental results not only show the effectiveness of the proposed approach but also demonstrate that it can significantly improve the performance of the retrieval system compared to other state-of-the-art algorithms.

Face-top coordination, which exists in most clothes-fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and tradeoffs between general preferences and individual preferences. We present a Deep-Based Self-Refined (DBSR) system to simulate face-top coordination based on intuition evaluation. To this end, we first establish a well-coordinated face-top (WCFT) dataset from fashion databases and communities. Then, we use a jointly trained CNN Deep Canonical Correlation Analysis (DCCA) method to bridge the semantic face-top gap based on the WCFT dataset to deal with general preferences. Subsequently, an irrelevance-based Optimum-path Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.

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 o...

The advancement in the field of remote sensing (RS) has offered a vast number of RS images with higher resolution. Nowadays, remote sensing image retrieval (RSIR) has become a challenging task for ...

A system has been developed to improve on the quality of search for which we have replaced the present searching methods by our procedure Explore Scheme, which greatly increases the utility of Web, over what is available today. None of the Search Techniques is able to deal effectively and efficiently with the huge volume of information posted on World Wide Web. In the current searching methodology, our proposed system emphasizes on enhancing the searching method by our proposed Explore-Scheme procedure, which provides solution to the complex queries based on contextual expansion. It is our observation that in investigating relevance feedback questions (both existing and new), we will implement the Explore-Scheme algorithm on web in this way we can enhance the ability which will give rise to the information which is good, of some use and relevant. Currently, we are implementing the system that is able to demonstrate important properties of the presented approach. Our method reduces the word to its atomic part thus curtailing the size of the dictionary by saving Storage space and processing time enhancing the processing speed of query. We can finally say that our proposed algorithm will definitely improve the information retrieval capabilities and are able to deal effectively and efficiently with the huge volume of information posted on World Wide Web.

The colour logo identification has one of the key problems of bridging the difference between low-level characteristics and high-level semantics, as the consumer wants to. Relevance feedback (RF) along with self-organizing map (SOM) have been successfully introduced to resolve this void. However, the efficiency of the automated map (SOM) based RF is always low when the feedback sample is limited. This paper proposed a new technology, namely the SOM-SOM-RF that combines SOM-based RF with warm particle optimization, to boost RF performance (PSO). The aim of this proposed technology is to increase SOM-based RF efficiency and also to minimise user encounters with the device by reducing its RF number. On 3000 colour logo pictures, the PSO-SOM-RF was tested. The findings from the tests revealed the high precision of the proposed PSO-SOM-RF.

Retrieving relevant documents from a large set using the original query is a formidable challenge. A generic approach to improve the retrieval process is realized using pseudo-relevance feedback techniques. This technique allows the expansion of original queries with conducive keywords that returns the most relevant documents corresponding to the original query. In this paper, five different hybrid techniques were tested utilizing traditional query expansion methods. Later, the boosting query term method was proposed to reweigh and strengthen the original query. The query-wise analysis revealed that the proposed approach effectively identified the most relevant keywords, and that was true even for short queries. All the proposed methods’ potency was evaluated on three different datasets; Roshni, Hamshahri1, and FIRE2011. Compared to the traditional query expansion methods, the proposed methods improved the mean average precision values of Urdu, Persian, and English datasets by 14.02%, 9.93%, and 6.60%, respectively. The obtained results were also established using analysis of variance and post-hoc analysis.

With the user’s feedback, projections are often used to reduce dimension and enhance class discrimination. The existing projections either use only the global euclidean structure or refer to the local manifold structure. However, global statistics such as variance (ie the method using the global euclidean structure) is difficult to estimate when there are not enough training samples. As for the methods that use the local manifold structure, the class discriminant is limited. In this paper, a Semantic Class Discriminant Projection (SCDP) is proposed for enhancing the performance of content-based image retrieval schemas with relevance feedback. SCDP can take advantage of the local geometry information of labeled and unlabeled images to learn a semantic subspace, and it obtains the most important properties of the subspaces to enhance classification. The experimental results performed on the two benchmark datasets have confirmed the superiority of the proposed method.

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.

Interactive face retrieval aims at finding target subjects in face databases through human and machine interaction, which involves user feedback based on human perception and machine similarity measure in feature spaces. In this article, we propose an attribute prototype learning method to tackle the semantic gap between human and machine in face perception for fast interactive face retrieval. We reformulate the theoretical explanation of the interactive retrieval model and develop the algorithm of the heuristic solution of the model. Each module of the prototype model is learned with a set of identity-related facial attributes. The outputs of the prototype modules form the semantic representation. To adapt the prototype models across different databases, we propose a transfer selection algorithm based on the coherence measurements in interactive face retrieval. Coherence analysis proves that the proposed attribute prototype representation can effectively narrow down the semantic gap even in the case of cross-database transfer learning. The prototype representation can effectively reduce the feature dimension in the retrieval process. Real user retrieval with the Bayesian relevance feedback model shows that attribute prototype space is superior to low-level feature space and proves that interactive retrieval with attribute prototype representation can converge fast in large face databases.

Content-based image retrieval (CBIR) is a promising technology to assist image finding, CBIR retrieves images by visual features inherent in images. Relevance feedback allows the user to reflect his preference to the system, then the system can reformulate the query according to the positive and/or negative examples responded by the user. This paper presents two efficient frameworks for image classification through the analysis of the visual features (such as color, shape, texture) of an example image. The first framework is based on comparing the norm and the direction of vectors ia multi-dimensional space for both the target and query image vectors, which allows classification according to their probabilities oli existence. The second framework depends on acquiring classification knowledge from a large empirical image database in a specific domain and utilizes that knowledge for image classification. The initial classification process is used as a training phase to feed the system with a classification tree for images in the retrieval domain. This tree is the best reduction of dimensionality that would result from all possible combinations of feature divisions.

With the large quantity of textual information produced on the Internet, a critical necessity is to filter out the irrelevant information and organize the rest into categories of interest (e.g., an emerging event). However, supervised-learning document filtering methods heavily rely on a large number of labeled documents for model training. Manually identifying plenty of positive examples for each category is expensive and time-consuming. Also, it is unrealistic to cover all the categories from an evolving text source that covers diverse kinds of events, user opinions, and daily life activities. In this article, we propose a novel attention-based deep relevance model for few-shot document filtering (named ADRM), inspired by the relevance feedback methodology proposed for ad hoc retrieval. ADRM calculates the relevance score between a document and a category by taking a set of seed words and a few seed documents relevant to the category. It constructs the category-specific conceptual representation of the document based on the corresponding seed words and seed documents. Specifically, to filter irrelevant yet noisy information in the seed documents, ADRM employs two types of attention mechanisms (namely whole-match attention and max-match attention ) and generates category-specific representations for them. Then ADRM is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process, a self-attention layer, and a relevance aggregation layer. Extensive experiments on three real-world datasets show that ADRM consistently outperforms the existing technical alternatives, including the conventional classification and retrieval baselines, and the state-of-the-art deep relevance ranking models for few-shot document filtering. We also perform an ablation study to demonstrate that each component in ADRM is effective for enhancing filtering performance. Further analysis shows that ADRM is robust under varying parameter settings.

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.

In this article, we explore the coherence of face perception between human and machine in the scenario of interactive face retrieval. In the part of human perception, we collect user feedback to the stimuli of a target face and groups of displayed candidate face images in a face database with a large number of subjects. In the part of machine vision, we compare the benchmark features and general metrics to measure face similarity. We propose a series of coherence measurements to evaluate the statistic characteristic of human and machine face perception. We discover that despite the unfamiliarity of users to most faces in the database, the coherence between human and machine remains in a stable level across multiple variations in metrics, features, size of databases, and demographics. The simulation experiments with the coherence distributions demonstrate that the embedded information is valuable to speed up interactive retrieval. The comparisons over multiple parameter settings provide feasible instructions in designing the interactive face retrieval system with more consideration of human factors.

Digital dictionaries have been widely used to facilitate word processing and word search through digital media such as mobile phones. Society generally knows the efficacy and how to mix medicinal plants from the experience of previous parents or through books and writings. Searching through books or writings requires a short time compared to searching through digital media, one of which is a digital dictionary. The research aims to create a digital dictionary of mobile-based medicinal plants which has a search facility based on the words entered, for example, the contents of the medicinal plants. The digital dictionary application of medicinal plants uses a search method with the Rocchio algorithm with relevance feedback techniques to check the proximity of the query to the average document relevant to the level of similarity calculation through the stages of tokenizing, filtering, stemming, and Term Weighting with a total data of 200 medicinal plants.

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve.

The modern world uses digital data invariably and the advent of smart phones and cameras add to images and communicating through images in social media is a very common happening due to the exposure to internet and the economical availability of data. Moreover, people explore the web for much news and on different topics. They feel satisfied to look into image type info than textual information. So, image retrieval is paramount nowadays and it is very much useful in many societal and defence applications. The methods used for retrieval started through text based and query based is popularly used now. The content based retrieval uses many stages, approaches, technologies, algorithms etc. It is intended to provide a broad perspective about CBIR in this work. Moreover, the computational and time complexities involved during retrieval relies on the performance of the entire system. One of the aspects that can improve the precision is the reduction of semantic gap. Taking this as the base, we have explored a literature and presented the points related to various points including relevance feedback so as to enable other researchers to carry out experimental work on the suggested areas.

Technological advances have evolved in all the directions including the biomedical, because of which a record number of lives are saved every day. The advancement has now surpassed the tools level, now the doctors with the help of new tools can also detect diseases, which saves the response time. In this paper, we will work on one such technique which will help in retrieving the similar type of images with the help of their features. In this paper, the features such as Texture features, LBP features, Retrieval feature, which are processed with hash coding and relevance feedback to get the final results. The framework provides the output utilizing a hash coding classifiers which predict the image from the database of the images. The images are classified on a global level with the help of multiple low-level features.

Analyzing and modeling user behavior in the Information Retrieval (IR) process is important for IR research. In this paper, we propose a new research schema in IR research. In this schema, we first investigate humans' cognitive process when completing a specific IR task and build cognitive models for that task. The findings in the investigation and the proposed cognitive models are then utilized to improve machines' performance in the IR task. Through this research schema, we revisit three IR tasks. In the first study, we carefully analyze users' clicking behavior in mobile search and propose a mobile click model to extract relevance feedback from the mobile search logs. In the second study, we conduct an eye-tracking study to investigate how human assessors read a document during relevance judgment task and adopt the findings in building a novel retrieval model that can better approximate humans' relevance judgment. In the last study, we conduct another eye-tracking study to investigate humans' reading behavior when completing the reading comprehension task. We build a prediction model for user attention and leverage the predicted attention signals to improve the machine reading comprehension model. By successfully adopting this research schema to three different IR tasks, we demonstrate its effectiveness and generalizability.

Over the past era, Frequent Pattern Mining (FPM) is emerging as a significant approach to discover fascinating knowledge concealed in the data. However, preceding works failed to address the validation of FPM with user queries and also achieving better scalability and execution time is still bottleneck owing to difficulties in handling large dataset. To address this downside, our proposed work establishes FPM using extend version of MapReduce framework in Hadoop environment. Our proposed work comprises of five processes that are: 1) Preprocessing 2) Affinity Propagation (AP) based Clustering 3) Load Balancing 4) Map-Optimize-Reduce 5) Mining User Queries. Primarily, our proposed work performs preprocessing to remove data redundancy. To speed up the MapReduce framework, we propose AP clustering which generates effective clusters from the given dataset. Load balancing is executed to balance load among different blocks concerning where reputation is computed. To avoid oversight in scanning and minimal searching space in MapReduce, optimizer is included between Mapper and Reducer where Emperor Penguin Colony (EPC) optimization is used. Frequent patterns are mined using CANonical order (CAN) tree based Frequent Pattern (FP) growth which reduces execution time and frequent tree construction. User provides Mining_Request to the Hadoop and frequent patterns are mined for given query which is send back to the user. If user given query is not present in the CAN tree, then it sends Relevance Feedback as a recommendation to the user. Finally, we validate our proposed work performance with the previous works for succeeding metrics that are Execution Time, Response Time, Load Balancing Rate, and Scalability.

Recently the collection of varied digital image databases has increased. Many users have found that searching and retrieving required images from large collections are very difficult tasks, and successful and effective retrieval methods were developed to provide an effective and efficient search and retrieval process. In most content-based image retrieval (CBIR) methods, various visual features have been considered indirect to retrieve the images from databases. Content-based medical image retrieval (CBMIR), like any CBIR method, is a technique for retrieving medical images on the basis of automatically derived image features, such as colour and texture. Although a number of methods and approaches have been suggested, retrieval performance remains one of the most challenging problems in current CBMIR studies, this due to the well-known `semantic gap' issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. To bridge this gap, much research has been proposed. This study proposes two expansion methods to increase and enhance precision of the retrieval model; both of our proposed methods depend on top-ranked images. Our first expansion method is to reformulate the new expand query image based on mean values for features of top-ranked images, while the second method is based on selecting the most important features only. The expansion process was performed on eighteen colour features and twelve texture features extracted from two common medical images datasets, Kvasir and PH2. Experimental results show that our proposed methods performed better and have a precision of 95.8% and 80.7% for Kvasir and PH2 data set, respectively.

Content-based image medical retrieval (CBMIR) is a technique for retrieving medical images on the basis of automatically derived image features such as colour, texture and shape. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. The retrieval performance of a CBMIR system depends mainly on the representation of image features, which researchers have studied extensively for decades. Although a number of methods and approaches have been suggested, it remains one of the most challenging problems in current (CBMIR) studies, largely due to the well-known “semantic gap” issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. There have been many techniques proposed to bridge this gap. This study proposes a novel relevance feedback retrieval method (RFRM) for CBMIR. The feedback implemented here is based on voting values performed by each class in the image repository. Here, eighteen using colour moments and GLCM texture features were extracted to represent each image and eight common similarity coefficients were used as similarity measures. After briefly researching using a single random image query, the top images retrieved from each class are used as voters to select the most effective similarity coefficient that will be used for the final searching process. Our proposed method is implemented on the Kvasir dataset, which has 4,000 images divided into eight classes and was recently widely used for gastrointestinal disease detection. Intensive statistical analysis of the results shows that our proposed RFRM method has the best performance for enhancing both recall and precision when it uses any group of similarity coefficients.

A method for image retrieval based on supervised local regression and global alignment (sLRGA) with relevance feedback for insect identification is presented in this paper. Based on the novel sLRGA, which is an extended version of LRGA, the proposed method estimates ranking scores for image retrieval in such a way that the neighborhood structure of a feature space of the database can be optimally preserved with consideration of class information. This is the main contribution of this paper. By measuring the relevance between all of the images and the query image in the database, sLRGA realizes accurate image retrieval. Furthermore, when positive/negative labels to retrieved images are given by users, the proposed method can improve image retrieval performance considering the query relevance information via use of both relevance feedback and sLRGA. This is the second contribution of this paper. Experimental results show the effectiveness of the proposed method.

In social image retrieval, the main goal is to offer a relevant but also diverse result set of images to the user. To address relevance and diversity at the same time, we propose a multi-modal procedure. This approach deals with the diversification problem using a two-step procedure based on the application of Formal Concept Analysis (FCA) to organize the text content of the images, followed by a Hierarchical Agglomerative Clustering (HAC) step to find the topics addressed by the images. FCA detects the latent concepts covered by the images in the result set, organizing them according to these concepts. In the second step, clustering is carried out to group together the ones with a similar concept. To assess the relevance, we use an adaptive multi-model relevance feedback algorithm which uses the low-level visual features to estimate a relevance measurement for all the images in the dataset. Several local logistic regression models are automatically adjusted to select the best performance for each topic. Finally, the images are ranked by selecting the highest probability image at each text cluster, generating a relevant but diverse ranked list. Diverse social images 2013, 2014, 2015, 2016 and 2017 datasets from the MediaEval benchmark are used to test the performance of our approach in a real scenario. Experimental results show that the proposed joint multimedia procedure works well on multi-concept and complex collections without adjusting parameters among collections, achieving the best results with a user-based relevance feedback algorithm. Our challenge is to achieve similar results with our automatic version.

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.

Recent advances in text representation have shown that training on large amounts of text is crucial for natural language understanding. However, models trained without predefined notions of topical interest typically require careful fine-tuning when transferred to specialized domains. When a sufficient amount of within-domain text may not be available, expanding a seed corpus of relevant documents from large-scale web data poses several challenges. First, corpus expansion requires scoring and ranking each document in the collection, an operation that can quickly become computationally expensive as the web corpora size grows. Relying on dense vector spaces and pairwise similarity adds to the computational expense. Secondly, as the domain concept becomes more nuanced, capturing the long tail of domain-specific rare terms becomes non-trivial, especially under limited seed corpora scenarios. In this paper, we consider the problem of fast approximate corpus expansion given a small seed corpus with a few relevant documents as a query, with the goal of capturing the long tail of a domain-specific set of concept terms. To efficiently collect large-scale domain-specific corpora with limited relevance feedback, we propose a novel truncated sparse document bit-vector representation, termed Signature Assisted Unsupervised Corpus Expansion (SAUCE). Experimental results show that SAUCE can reduce the computational burden while ensuring high within-domain lexical coverage., Accepted to CIKM'21 Applied Research Track

With the advent of post-web, emerging large-scale IR systems began to be strictly differentiated from simple applications that favored the realization of “Memorization” in the traditional sense, and transformed to predictive tools with the mission of “Generalization”. [1] Feedback from users in the direction of personalized customization in the IR field is a far-reaching example under the influence of this idea. In particular, what this paper wants to focus on is implicit feedback.[2]In this paper, you will see how the development of some other prerequisites in the IR field has profoundly affected the shaping of personalized customized services. Besides, the paper will also show an improved relevance feedback framework for analyzing the personalized services. By constructing the feature table, two types of features are added to the representation process in this paper to improve the accuracy of BM25 algorithm. After DCG calculations., this will ultimately improve the accuracy of the rankings.

Sketch-based Image Retrieval (SBIR) is an approach where natural images are retrieved according to the given input sketch query. SBIR has many applications, for example, searching for a product given the sketch pattern in digital catalogs, searching for missing people given their prominent features from a digital people photo repository etc. The main challenge involved in implementing such a system is the absence of semantic information in the sketch query. In this work, we propose a combination of image prepossessing and deep learning-based methods to tackle this issue. A binary image highlighting the edges in the natural image is obtained using Canny-Edge detection algorithm. The deep features were extracted by an ImageNet based CNN model. Cosine similarity and Euclidean distance measures are adopted to generate the rank list of candidate natural images. Relevance feedback using Rocchio’s method is used to adapt the query of sketch images and feature weights according to relevant images and non-relevant images. During the experimental evaluation, the proposed approach achieved a Mean average precision (MAP) of 71.84%, promising performance in retrieving relevant images for the input query sketch images.

We present Peax, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user’s perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users’ perceived similarity. Using an active learning sampling strategy, Peax collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate Peax’s features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of Peax. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.

This article describes an information retrieval system with entity query expansion by relevance feedback. The performance of the system is tested assuming its usage as a support tool for lawyers constructing a legal framework for a case. The objective is to improve the precision of results when searching for relevant jurisprudence. For this, the entities belonging to a knowledge base are used as a means to expand the query. The expansion can be done using either an automatic or an interactive mechanism. This second approach suggests to the user concepts related to the query, which might improve the search experience. An ontology and a knowledge base, called LegalOnto and LegalBase, respectively, were developed. The ontology includes concepts not addressed by existing legal ontologies, and the knowledge base integrates LegalOnto with the thesaurus of the Argentine System of Legal Information (Sistema Argentino de Información Jurídica: SALT), enriched in the subject of traffic accidents. Quantitative experimentation is carried out upon a set of court documents that are used to populate the knowledge base. Preliminary results are encouraging., En este artículo se presenta un sistema de recuperación de información con expansión de la consulta basada en entidades mediante la retroalimentación por relevancia. Se propone una herramienta para los abogados que facilite la construcción del marco legal de un caso. El objetivo del sistema de búsqueda es mejorar la precisión de los resultados en la búsqueda de jurisprudencias relevantes. Para esto, se utilizan las entidades pertenecientes a una base de conocimiento como medio para reformular la consulta. La expansión puede realizarse mediante mecanismos automáticos o interactivos. Esta última opción puede sugerirle al usuario conceptos relacionados a su consulta, lo cual puede mejorar su experiencia de búsqueda. Para esta aplicación se construyeron una ontología y una base de conocimiento, llamadas LegalOnto y LegalBase respectivamente. La ontología incluye conceptos que no se encuentran en ontologías legales existentes y la base de conocimiento integra a LegalOnto junto con el tesauro del Sistema Argentino de Información Jurídica (SAIJ), enriquecido con conceptos pertenecientes al ámbito de los accidentes de tránsito. Se realizaron evaluaciones cuantitativas de los modelos de búsqueda propuestos sobre un conjunto de sumarios, los cuales también fueron utilizados para poblar la base de conocimiento. Los resultados preliminares obtenidos son alentadores., Facultad de Informática

Normalized discounted cumulative gain (NDCG) is one of the popular evaluation metrics for recommender systems and learning-to-rank problems. As it is non-differentiable, it cannot be optimized by gradient-based optimization procedures. In the last twenty years, a plethora of surrogate losses have been engineered that aim to make learning recommendation and ranking models that optimize NDCG possible. However, binary relevance implicit feedback settings still pose a significant challenge for such surrogate losses as they are usually designed and evaluated only for multi-level relevance feedback. In this paper, we address the limitations of directly optimizing the NDCG measure by proposing a guided learning approach (GuidedRec) that adopts recent advances in parameterized surrogate losses for NDCG. Starting from the observation that jointly learning a surrogate loss for NDCG and the recommendation model is very unstable, we design a stepwise approach that can be seamlessly applied to any recommender system model that uses a point-wise logistic loss function. The proposed approach guides the models towards optimizing the NDCG using an independent surrogate-loss model trained to approximate the true NDCG measure while maintaining the original logistic loss function as a stabilizer for the guiding procedure. In experiments on three recommendation datasets, we show that our guided surrogate learning approach yields models better optimized for NDCG than recent state-of-the-art approaches using engineered surrogate losses.

Motivated by recent commentary that has questioned today's pursuit of ever-more complex models and mathematical formalisms in applied machine learning and whether meaningful empirical progress is actually being made, this paper tackles the decades-old problem of pseudo-relevance feedback with "the simplest thing that can possibly work". We present a technique based on training a document relevance classifier for each information need using pseudo-labels from an initial ranked list and then applying the classifier to rerank the retrieved documents. Experiments demonstrate significant improvements across a number of standard newswire collections, with initial rankings supplied by bag-of-words BM25 as well as from query expansion. Further evaluations in the TREC-COVID challenge using human relevance judgments verify the effectiveness and robustness of our proposed technique. While this simple idea draws elements from several well-known threads in the literature, to our knowledge this exact combination has not previously been proposed and rigorously evaluated.

In general, hadith consists of isnad and matan (content). Matan can be separated into several components for example a story, main content, and some additional information. Other texts besides main content, such as isnad and story can interfere the retrieval process of relevant documents because most users typically use simple queries. Thus, in this paper, we proposed a Named Entity Recognition (NER) component weighting model in improving the Indonesian hadith retrieval system. We did 3 test scenarios, the first scenario (S1) did not separate the hadith into several components, the second scenario (S2) separated the hadith into 2 components, isnad and matan, and the third scenario separated the hadith into 4 components, isnad, background story, content, and additional information. From the experimental results, it is found that the TF-IDF with rocchio algorithm in query expansion outperforms DocVec. Also, separation and weighting of the hadith components affect the retrieval performance because isnad can be considered as noise in a query. Separation of 2 separate components had the best overall results in general although 4 separate components showed better results in some cases with precision up to 100% and 70% recall.

Document retrieval methods that utilize relevance feedback often induce a single query model from the set of feedback documents, specifically, the relevant documents. We empirically show that for a few state-of-the-art query-model induction methods, retrieval performance can be significantly improved by constructing the query model from a subset of the relevant documents rather than from all of them. Motivated by this finding, we propose a new approach for relevance-feedback-based retrieval. The approach, derived from the risk minimization framework, is based on utilizing multiple query models induced from all subsets of the given relevant documents. Empirical evaluation shows that the approach posts performance that is statistically significantly better than that of applying the standard practice of utilizing a single query model induced from the relevant documents. While the average relative improvements are small to moderate, the robustness of the approach is substantially higher than that of a variety of reference comparison methods that address various challenges in using relevance feedback.

A series of click models has been proposed to extract accurate and unbiased relevance feedback from valuable yet noisy click-through data in search logs. Previous works have shown that users search behavior in mobile and desktop scenarios are rather different in many aspects, therefore, the click models designed for desktop search may not be effective in the mobile context. To address this problem, we propose two novel click models for mobile search: (1) Mobile Click Model (MCM), which models click necessity bias and examination satisfaction bias; (2) Viewport Time Click Model (VTCM), which further extends MCM by utilizing the viewport time. Extensive experiments on large-scale real mobile search logs show that: (1) MCM and VTCM outperform existing models in predicting users’ clicks and estimating result relevance; (2) MCM and VTCM can extract richer information, such as the click necessity of search results and the probability of user satisfaction, from mobile click logs; (3) By modeling the viewport time distributions of heterogeneous results, VTCM can bring a significant improvement over MCM in click prediction and relevance estimation tasks. Our proposed click models can help better understand user behavior patterns in mobile search and improve the ranking performance of mobile search engines.

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.

One of the cornerstones of content-based image retrieval (CBIR) for medical image diagnosis is to select the images that present higher similarity with a given query image. Different from previous literature efforts, the present work aims to seamlessly fuse a powerful machine learning strategy based on the active learning paradigm, in order to obtain greater efficacy regarding similarity queries in medical CBIR systems. To do so, we propose a new approach, named as Medical Active leaRning and Retrieval (MARRow) to aid the breast cancer diagnosis. It enables to deal with more feasible strategies, specifically for the medical context and its inherent constraints. We also proposed an active learning strategy to select a small set of more informative images, considering selection criteria based on not only similarity, but also on certain degrees of diversity and uncertainty. To validate our proposed approach, we performed experiments using public medical image datasets, different descriptors for each one and compared our approach against four widely applied and well-known literature approaches, such as: Traditional CBIR without relevance feedback strategies, Query Point Movement Strategy (QPM), Query Expansion (QEX) and SVM Active Learning (SVM-AL). From the experiments, we can observe that our approach presents a strong performance over state-of-the-art ones reaching a precision gain of up to 87.3%. MARRow also presented a well-suited and consistent increasing rate along the learning iterations. Moreover, our approach can significantly minimize the expert’s involvement in the analysis and annotation process (reducing up to 88%). The results testify that MARRow improves the precision of the similarity queries. It is capable to explore at the maximum the experts’ intentions, which are captured during the relevance feedback process, incrementally improving the learning model. Therefore, our approach can be suitable and applied in challenging processes, such as real and medical contexts, enhancing medical decision support systems (e.g. breast cancer diagnosis).

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., 25 pages

Grasping the sense of web pages on small screen of mobile devices is too difficult, so mobile users prefer summarized reports. One popular technique to generate automatic summarized report is based on vector model. The vector model summarization methods are simple and low cost to implement on mobile devices. However, the quality of summarized reports of the model may not be good because there is a semantic difference between manual summarized reports and the machine summarized reports. In addition, the censored documents of the centralized search engine cannot accurately reflect the query of user in the summary results. To overcome this constraints, automatic mobile device summarizer using a semantic feature of pseudo relevance feedback and P2P web search engine is proposed. The proposed method increases the quality of summarized reports by using the latent features of documents and clustering technique. The method uses none censored documents of the P2P web search engine to more accurately reflect user requirements in summarized reports. The automatic summarizer consists of a server-side containerized auto-summary module for flexible management of the summarization functions and a user-side mobile device module to reduce the overload of the mobile device.

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.