Results 221 to 230 of about 261,614 (261)

Relevance Feedback for XML Retrieval

2005
Relevance Feedback (RF) algorithms were studied in the context of traditional IR systems where the returned unit is an entire document. In this paper we describe a component ranking algorithm for XML retrieval and show how to apply known RF algorithms from traditional IR on top of it to achieve Relevance Feedback for XML.
Yosi Mass, Matan Mandelbrod
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Alternating feature spaces in relevance feedback

Proceedings of the 2001 ACM workshops on Multimedia multimedia information retrieval - MULTIMEDIA '01, 2001
Image retrieval using relevance feedback can be considered as a classification process. In practice, the generalization of classifier is often constrained by the insufficiency of training samples. In this paper, we propose a novel relevance feedback approach capable of collecting more representative samples.
Fang Qian, Mingjing Li, HongJiang Zhang, Wei-Ying Ma, Bo Zhang 0010   +4 more
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Utilizing Focused Relevance Feedback

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016
We present a novel study of ad hoc retrieval methods utilizing document-level relevance feedback and/or focused relevance feedback; namely, passages marked as (non-)relevant. The first method uses a novel mixture model that integrates relevant and non-relevant information at the language model level. The second method fuses retrieval scores produced by
Elinor Brondwine, Anna Shtok, Oren Kurland   +2 more
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Relevance feedback at INEX 2005

ACM SIGIR Forum, 2006
Relevance feedback in the INEX environment is addressed by several papers in the proceedings of the INEX 2005 Workshop [1]. The most extensive discussion is provided by Schenkel and Theobald [2]. Mihajlovic, et. al. [3], and Sauvagnat, et. al. [4], describe approaches to relevance feedback in
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Mixture of KL subspaces for relevance feedback

Multimedia Tools and Applications, 2007
Relevance feedback has recently emerged as a solution to the problem of improving the retrieval performance of an image retrieval system based on low-level information such as color, texture and shape features. Most of the relevance feedback approaches limit the utilization of the user's feedback to a single search session, performing a short-term ...
FRANCO, ANNALISA, LUMINI, ALESSANDRA
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3D relevance feedback via multilevel relevance judgements

The Visual Computer, 2010
Relevance feedback techniques are expected to play an important role in 3D search engines, as they help to bridge the semantic gap between the user and the system. Indeed, similarity is a cognitive process that depends on the observer. We propose a novel relevance feedback technique, which relies on the assumption that similarity may emerge from the ...
Daniela Giorgi, Patrizio Frosini, Michela Spagnuolo, Bianca Falcidieno   +3 more
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THE ESTIMATION OF TERM RELEVANCE WEIGHTS USING RELEVANCE FEEDBACK

Journal of Documentation, 1981
The term relevance weighting method has been shown to produce optimal information retrieval queries under well‐defined conditions. Unfortunately, the relevance weights cannot be determined in the absence of accurate knowledge of the occurrence frequencies of the terms in the relevant and non‐relevant documents of a collection.
Harry Wu, Gerard Salton
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Multimedia Search with Pseudo-relevance Feedback

2003
We present an algorithm for video retrieval that fuses the decisions of multiple retrieval agents in both text and image modalities. While the normalization and combination of evidence is novel, this paper emphasizes the successful use of negative pseudo-relevance feedback to improve image retrieval performance.
Rong Yan, Alexander G. Hauptmann, Rong Jin 0001   +2 more
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Negative Samples Analysis in Relevance Feedback

IEEE Transactions on Knowledge and Data Engineering, 2007
Recently, relevance feedback (RF) in content-based image retrieval (CBIR) has been implemented as an online binary classifier to separate the positive samples from the negative samples, where both sets of samples are labeled by the user. In many applications, it is reasonable to assume that all the positive samples are alike and thus that the region of
Dacheng Tao, Xuelong Li 0001, Stephen J. Maybank   +2 more
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Clustered Semi-Supervised Relevance Feedback

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015
In relevance feedback, first-round search results are used to boost second-round search results. Two forms have been traditionally considered: exhaustively labelled feedback, where all first-round results to depth k are annotated for relevance by the user; and blind feedback, where the top-k results are all assumed to be relevant.
Kripabandhu Ghosh, Swapan Kumar Parui
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