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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.

Recent technological advancements have led to a significant increase in the quantity and accessibility of videos. The decrease in video acquisition costs and the increase in memory capacity have made it possible to store large video collections in computer systems. To effectively exploit these collections, it is crucial to have tools that facilitate access and management. In this paper, we present a multimedia retrieval approach that prioritizes the user's needs by starting with a text-based query. The approach consists of two main parts: (i) a new multi-level and deep-semantic video classification indexing method, and (ii) a query expansion mechanism and relevance feedback system to improve the results based on the user's feedback. Our contribution is demonstrated through the implementation of the Deep-VISEN prototype and experiments on a collection of 2700 videos and 62838 images. The results show that our algorithm is effective and precise. [ABSTRACT FROM AUTHOR]

The semantic gap between low-level features and high-level semantic concepts is a fundamental problem in content-based image retrieval (CBIR). To close this gap, relevant feedback is included in the CBIR. Based on the user's feedback samples, a projection matrix is learned to project samples from the multi-dimensional original space to the low-dimensional projection space, and a classifier is learned on the projection space to classify the images. However, the number of classes in the relevance feedback is very small (only two classes), which leads to low classification performance and results in poor retrieval performance. To solve this problem, we propose a novel supervised image retrieval method, called Sparse Discriminant Analysis for Image Retrieval (SDAIR). Different from existing image retrieval methods, which have low precision due to small-class size problems, SDAIR is designed to not be affected by small-class size problems. Therefore, SDAIR is potentially more suitable for image retrieval with relevant feedback, where class sizes are often very small. The experimental results on the two databases demonstrate that the proposed method obtains competitive precision compared with other content-based image retrieval methods. [ABSTRACT FROM AUTHOR]

One of the main challenges in CBIR systems is to choose discriminative and compact features, among dozens, to represent the images under comparison. Over the years, a great effort has been made to combine multiple features, mainly using early, late, and hierarchical fusion techniques. Unveiling the perfect combination of features is highly domain-specific and dependent on the type of image. Thus, the process of designing a CBIR system for new datasets or domains involves a huge experimentation overhead, leading to multiple fine-tuned CBIR systems. It would be desirable to dynamically find the best combination of CBIR systems without needing to go through such extensive experimentation and without requiring previous domain knowledge. In this paper, we propose ExpertosLF, a model-agnostic interpretable late fusion technique based on online learning with expert advice, which dynamically combines CBIR systems without knowing a priori which ones are the best for a given domain. At each query, ExpertosLF takes advantage of user's feedback to determine each CBIR contribution in the ensemble for the following queries. ExpertosLF produces an interpretable ensemble that is independent of the dataset and domain. Moreover, ExpertosLF is designed to be modular, and scalable. Experiments on 13 benchmark datasets from the Biomedical, Real, and Sketch domains revealed that: (i) ExpertosLF surpasses the performance of state of the art late-fusion techniques; (ii) it successfully and quickly converges to the performance of the best CBIR sets across domains without any previous domain knowledge (in most cases, fewer than 25 queries need to receive human feedback). [ABSTRACT FROM AUTHOR]

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples.

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]

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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 Informacion ´ Jur´ıdica: SAIJ), 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.

Relevance is one of the concepts that many researchers consider it fundamental in information retrieval. Recently, relevance has also been given attention in Iran about which multiple studies have been conducted. However, there is no clear understanding of relevance-related studies in Iran. Therefore, the current research aimed at exploring related research through a systematic review. To find the publications, questions and inclusion/exclusion criteria were initially determined. Several databases were searched using related keywords. Finally, 108 publications were selected. The software Excel was used to extract and analyze the data gathered. Publications in the field of relevance could be classified into 6 groups including recall, precision, recall and precision, relevance feedback, relevance judgment, and review resources. The results showed that most of the studies are conducted in an objective way and the researchers considered search engines more than other information retrieval systems. Moreover, the results also indicate that the recent studies are related to relevance judgment and experts in Knowledge and Information Science have been unwilling to do research on relevance feedback. While the quantitative methodology is used more frequently, the qualitative approach could also be very effective, especially in the subjective relevance and user-friendly context.

Befitting from the interpretability and the capacity in capturing the underlying manifold structure, diffusion process (DP) has attracted increasing attention in the field of image retrieval. Within it, hierarchical diffusion process (HDP) has achieved satisfactory results in retrieved performance and complexity. However, the existing hierarchical diffusion process methods only diffuse the affinity values in low-level visual space without considering the high-level semantic information, which cause the problem of semantic gap. To overcome these problems, we propose a Graph Regularized Hierarchical Diffusion Process (GRHDP) method with relevance feedback, and apply it to retrieve medical images. The proposed algorithm firstly establishes a hierarchical structure of the images in medical image database and spreads the affinity values among query images and top-layer images by graph regularization diffusion. Then relevance feedback is introduced to adjust the similarity between query images and retrieved images in top layer, and the affinity values are diffused again according to labeled information of feedback. Finally, the similarity between queries and others in database can be obtained by interpolating the diffused results on the top layer from top to bottom. The experimental results show that our proposed GRHDP with relevance feedback has achieved better retrieval performance than manifold ranking and regularized diffusion process (RDP) when returning top retrieved images.

Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization of the images. Different feature extraction technique have been proposed to overcome the semantic breach problems, however these methods suffer from several shortcomings. This paper contributes an image retrieval system to extract the local features based on the fusion of scale-invariant feature transform (SIFT) and KAZE. The strength of local feature descriptor SIFT complements global feature descriptor KAZE. SIFT concentrates on the complete region of an image using high fine points of features and KAZE ponders on details of a boundary. The fusion of local feature descriptor and global feature descriptor boost the retrieval of images having diverse semantic classification and also helps in achieving the better results in large scale retrieval. To enhance the scalability of image retrieval bag of visual words (BoVW) is mainly used. The fusion of local and global feature representations are selected for image retrieval for the reason that SIFT effectively captures shape and texture and robust towards the change in scale and rotation, while KAZE have strong response towards boundary and changes in illumination. Experiments conducted on two image collections, namely, Caltech-256 and Corel 10k demonstrate the proposed scheme appreciably enhanced the performance of the CBIR compared to state-of-the-art image retrieval techniques. [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]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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.

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.

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]

Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track. [ABSTRACT FROM AUTHOR]