相关论文
2005 - ISVC
An Automatic Relevance Feedback in Image Retrieval Using Belief Functions

This paper proposes an automatic relevance feedback approach for content-based image retrieval using information fusion and without any user input. This method is proposed as an alternative of the simple ranking of result images. The idea consists to pass from a simple user selected query image to multi-images query in order to get more information about the query image type. Given a query image, the system first computes its feature vector to rank the images according to a well-chosen similarity measure. For each retrieved image, the degree of belief about the relevance is then assigned as a function of this measure. This degree of belief is then updated using an iterative process. At each iteration, we evaluate, for each retrieved image, the degree of relevance using the combination of belief functions associated to previously retrieved images. Then, each retrieved image is not found by the query image only but it is found by the query image and previously retrieved images too. Some experimental results will be proposed in this paper in order to demonstrate that the methodology improves the efficiency and accuracy of retrieval systems.

2010 - ArXiv
An Architecture of Active Learning SVMs with Relevance Feedback for Classifying E-mail

In this paper, we have proposed an architecture of active learning SVMs with relevance feedback (RF)for classifying e-mail. This architecture combines both active learning strategies where instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels of some number of them and relevance feedback where if any mail misclassified then the next set of support vectors will be different from the present set otherwise the next set will not change. Our proposed architecture will ensure that a legitimate e-mail will not be dropped in the event of overflowing mailbox. The proposed architecture also exhibits dynamic updating characteristics making life as difficult for the spammer as possible.

2010 - 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering
Combined automatic weighting and relevance feedback method in Content-Based Image Retrieval

Relevance Feedback (RF) is a powerful technique in Content-Based Image Retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a combined automatic weighting and relevance feedback method is proposed to improve the retrieval performance of CBIR. An approach using genetic algorithm for computing the initial weight of feature vector was introduced. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.

2009 - 2009 International Conference on Future BioMedical Information Engineering (FBIE)
The application of particle swarm optimization in relevance feedback

Particle swarm optimization(PSO) algorithm is applied in relevance feedback(RF) of image retrieval, which is combined with user feedback process effectively, The proposed method makes users' understanding to the retrieval goal become clear gradually, it could search image database effectively and comprehensively, and at the same time it could avoid the contradiction of efficiency and retrieval effect caused by multi-feedback. Experiments show that the proposed algorithm is validity.

2008
Incorporating relevance and psuedo-relevance feedback in the markov random field model: Brown at the TREC'08 relevance feedback track

We present a new document retrieval approach combining relevance feedback, pseudo-relevance feedback, and Markov random field modeling of term interaction. Overall effectiveness of our combined model and the relative contribution from each component is evaluated on the GOV2 webpage collection. Given 0-5 feedback documents, we find each component contributes unique value to the overall ensemble, achieving significant improvement individually and in combination. Comparative evaluation in the 2008 TREC Relevance Feedback track further shows our complete system typically performs as well or better than peer systems.

2005 - COCOON
A Quadratic Lower Bound for Rocchio's Similarity-Based Relevance Feedback Algorithm

It is shown in [4] that Rocchio's similarity-based relevance feedback algorithm makes Ωn mistakes in searching for a collection of documents represented by a monotone disjunction of at most k relevant features or terms over the n-dimensional binary vector space {0, 1}n. In practice, Rocchio's algorithm often uses a fixed query updating factor and a fixed classification threshold. When this is the case, we strengthen the work in [4] in this paper and prove that Rocchio's algorithm makes Ωkn---k mistakes in searching for the same collection of documents over the binary vector space {0, 1}n. A quadratic lower bound is obtained when k is proportional to n. An Okn---k2 upper bound is also obtained.

2008 - Inf. Process. Manag.
A new robust relevance model in the language model framework

In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.

2007 - ICIAR
Image Retrieval Using Transaction-Based and SVM-Based Learning in Relevance Feedback Sessions

This paper introduces a composite relevance feedback approach for image retrieval using transaction-based and SVM-based learning. A transaction repository is dynamically constructed by applying these two learning techniques on positive and negative session-term feedback. This repository semantically relates each database image to the query images having been used to date. The query semantic feature vector can then be computed using the current feedback and the semantic values in the repository. The correlation measures the semantic similarity between the query image and each database image. Furthermore, the SVM is applied on the session-term feedback to learn the hyperplane for measuring the visual similarity between the query image and each database image. These two similarity measures are normalized and combined to return the retrieved images. Our extensive experimental results show that the proposed approach offers average retrieval precision as high as 88.59% after three iterations. Comprehensive comparisons with peer systems reveal that our system yields the highest retrieval accuracy after two iterations.

2004 - EDBT Workshops
Relevance Feedback in XML Retrieval

Highly heterogeneous XML data collections that do not have a global schema, as arising, for example, in federations of digital libraries or scientific data repositories, cannot be effectively queried with XQuery or XPath alone, but rather require a ranked retrieval approach As known from ample work in the IR field, relevance feedback provided by the user that drives automatic query refinement or expansion can often lead to improved search result quality (e.g., precision or recall) In this paper we present a framework for feedback-driven XML query refinement and address several building blocks including reweighting of query conditions and ontology-based query expansion We point out the issues that arise specifically in the XML context and cannot be simply addressed by straightforward use of traditional IR techniques, and we present our approaches towards tackling them.

2004 - AWIC
Extending the Rocchio Relevance Feedback Algorithm to Provide Contextual Retrieval

Contextual retrieval supports differences amongst users in their information seeking requests. The Web, which is very dynamic and nearly universally accessible, is an environment in which it is increasingly difficult for users to find documents that satisfy their specific information needs. This problem is amplified as users tend to use short queries. Contextual retrieval attempts to address this problem by incorporating knowledge about the user and past retrieval results in the search process. In this paper we explore a feedback technique based on the Rocchio algorithm that significantly reduces demands on the user while maintaining comparable performance on the Reuters-21578 corpus.

2010 - ETRA
Eye movement as an interaction mechanism for relevance feedback in a content-based image retrieval system

Relevance feedback (RF) mechanisms are widely adopted in Content-Based Image Retrieval (CBIR) systems to improve image retrieval performance. However, there exist some intrinsic problems: (1) the semantic gap between high-level concepts and low-level features and (2) the subjectivity of human perception of visual contents. The primary focus of this paper is to evaluate the possibility of inferring the relevance of images based on eye movement data. In total, 882 images from 101 categories are viewed by 10 subjects to test the usefulness of implicit RF, where the relevance of each image is known beforehand. A set of measures based on fixations are thoroughly evaluated which include fixation duration, fixation count, and the number of revisits. Finally, the paper proposes a decision tree to predict the user's input during the image searching tasks. The prediction precision of the decision tree is over 87%, which spreads light on a promising integration of natural eye movement into CBIR systems in the future.

2000 - VISUAL
Content-Based Image Retrieval by Relevance Feedback

Relevance feedback is a powerful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the interweights of integrated probability function by using the information of both relevant and irrelevant retrieved images. Experimental results have shown the effectiveness and robustness of our proposed approach, especially in the situation of no relevant retrieved images.

2001
A model of cognitive load for IR: implications for user relevance feedback interaction

Preliminary experimentation demonstrates that although a system that allows for context specific spontaneity is likely to improve the acquisition of user relevance feedback, this is by no means a solution. We believe that the problems associated with a lack of willingness to modify queries or to provide relevance feedback within the Web environment, are indicative of a high state of cognitive load. A number of task and cognitive variables exist and need to be identified before a model of cognitive load for IR can be developed.

2001 - MULTIMEDIA '01
Adaptive nearest neighbor search for relevance feedback in large image databases

Relevance feedback is often used in refining similarity retrievals in image and video databases. Typically this involves modification to the similarity metrics based on the user feedback and recomputing a set of nearest neighbors using the modified similarity values. Such nearest neighbor computations are expensive given that typical image features, such as color and texture, are represented in high dimensional spaces. Search complexity is a ciritcal issue while dealing with large databases and this issue has not received much attention in relevance feedback research. Most of the current methods report results on very small data sets, of the order of few thousand items, where a sequential (and hence exhaustive search) is practical. The main contribution of this paper is a novel algorithm for adaptive nearest neigbor computations for high dimensional feature vectors and when the number of items in the databse is large. The proposed method exploits the correlations between two consecutive nearest neighbor searches when the underlying similarity metric is changing, and filters out a significant number of candidates ina two stage search and retrieval process, thus reducing the number of I/O accesses to the database. Detailed experimental results are provided using a set of about 700,000 images. Comparision to the existing method shows an order of magnitude overall imporovement.

2002 - Inf. Process. Manag.
An experiment in building profiles in information filtering: the role of context of user relevance feedback

An experiment was conducted to see how relevance feedback could be used to build and adjust profiles to improve the performance of filtering systems. Data was collected during the system interaction of 18 graduate students with SIFTER (Smart Information Filtering Technology for Electronic Resources), a filtering system that ranks incoming information based on users' profiles. The data set came from a collection of 6000 records concerning consumer health. In the first phase of the study, three different modes of profile acquisition were compared. The explicit mode allowed users to directly specify the profile; the implicit mode utilized relevance feedback to create and refine the profile; and the combined mode allowed users to initialize the profile and to continuously refine it using relevance feedback. Filtering performance, measured in terms of Normalized Precision, showed that the three approaches were significantly different (α = 0.05 and p = 0.012). The explicit mode of profile acquisition consistently produced superior results. Exclusive reliance on relevance feedback in the implicit mode resulted in inferior performance. The low performance obtained by the implicit acquisition mode motivated the second phase of the study, which aimed to clarify the role of context in relevance feedback judgments. An inductive content analysis of thinking aloud protocols showed dimensions that were highly situational, establishing the importance context plays in feedback relevance assessments. Results suggest the need for better representation of documents, profiles, and relevance feedback mechanisms that incorporate dimensions identified in this research.

2000 - Journal of the Association for Information Science and Technology
Interactive query expansion: A user-based evaluation in a relevance feedback environment

A user-centered investigation of interactive query expansion within the context of a relevance feedback system is presented in this article. Data were collected from 25 searches using the INSPEC database. The data collection mechanisms included questionnaires, transaction logs, and relevance evaluations. The results discuss issues that relate to query expansion, retrieval effectiveness, the correspondence of the on-line-to-off-line relevance judgments, and the selection of terms for query expansion by users (interactive query expansion). The main conclusions drawn from the results of the study are that: (1) one-third of the terms presented to users in a list of candidate terms for query expansion was identified by the users as potentially useful for query expansion. (2) These terms were mainly judged as either variant expressions (synonyms) or alternative (related) terms to the initial query terms. However, a substantial portion of the selected terms were identified as representing new ideas. (3) The relationships identified between the five best terms selected by the users for query expansion and the initial query terms were that: (a) 34% of the query expansion terms have no relationship or other type of correspondence with a query term; (b) 66% of the remaining query expansion terms have a relationship to the query terms. These relationships were: narrower term (46%), broader term (3%), related term (17%). (4) The results provide evidence for the effectiveness of interactive query expansion. The initial search produced on average three highly relevant documents; the query expansion search produced on average nine further highly relevant documents. The conclusions highlight the need for more research on: interactive query expansion, the comparative evaluation of automatic vs. interactive query expansion, the study of weighted Web-based or Web-accessible retrieval systems in operational environments, and for user studies in searching ranked retrieval systems in general.

2002 - Object recognition supported by user interaction for service robots
Building a latent semantic index of an image database from patterns of relevance feedback

This paper proposes a novel view of the information generated by relevance feedback. The latent semantic analysis is adapted to this view to extract useful inter-query information. The view presented in this paper is that the fundamental vocabulary of the system is the images in the database and that relevance feedback is a document whose words are the images. A relevance feedback document contains the intra-query information which expresses the semantic intent of the user over that query. The inter-query information then takes the form of a collection of documents which can be subjected to latent semantic analysis. An algorithm to query the latent semantic index is presented and evaluated against real data sets.

2006 - Signal Process. Image Commun.
Evaluation of relevance feedback schemes in content-based in retrieval systems

Abstract Multimedia content modeling, i.e., identification of semantically meaningful entities, is an arduous task mainly due to the fact that (a) humans perceive the content using high-level concepts and (b) the subjectivity of human perception, which often interprets the same content in a different way at different times. For this reason, an efficient content management system has to be adapted to current user's information needs and preferences through an on-line learning strategy based on users’ interaction. One adaptive learning strategy is relevance feedback, originally developed in traditional text-based information retrieval systems. In this way, the user interacts with the system to provide information about the relevance of the content, which is then fed back to the system to update its performance. In this paper, we evaluate and investigate three main types of relevance feedback algorithms; the Euclidean, the query point movements and the correlation-based approaches. In the first case, we examine heuristic and optimal techniques which are based either on the weighted or the generalized Euclidean distance. In the second case, we survey single and multipoint query movement schemes. As far as the third type is concerned, two different ways for parametrizing the normalized cross-correlation similarity metric are proposed. The first scales only the elements of the query feature vector and called query-scaling strategy, while the second scales both the query and the selected samples (query-sample scaling strategy). All the examined algorithms are evaluated using both subjective and objective criteria. Subjective evaluation is performed by depicting the best retrieved images as response of the system to a user's query. Instead, objective evaluation is obtained using standard criteria, such as the precision–recall curve and the average normalized modified retrieval rank (ANMRR). Furthermore, a newly objective criterion, called average normalized similarity metric distance is introduced which exploits the difference among the actual and ideal similarity measure among all best retrievals. Discussions and comparisons of all the aforementioned relevance feedback algorithms are presented.

2002 - ECIR
The Use of Implicit Evidence for Relevance Feedback in Web Retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness.

2011 - IEEE Transactions on Knowledge and Data Engineering
Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

The Probabilistic Relevance Framework: BM25 and Beyond

Hugo Zaragoza
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Stephen Robertson
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S. Robertson
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H. Zaragoza

Abstract:The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970—1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.

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引用
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