Results 261 to 270 of about 1,130,991 (283)

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, Wei-Ying Ma, Fuzong Lin, Bo Zhang   +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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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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Elicitation of term relevance feedback

Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 2006
Term relevance feedback has had a long history in information retrieval. However, research on interactive term relevance feedback has yielded mixed results. In this paper, we investigate several aspects related to the elicitation of term relevance feedback: the display of document surrogates, the technique for identifying or selecting terms, and ...
Diane Kelly, Xin Fu
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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.
Yan, Rong, Hauptmann, Alexander, Jin, Rong   +2 more
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Semantic propagation from relevance feedbacks

2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2005
Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedback provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database, given a set of relevance ...
null Hoon Yul Bang, null Cha Zhang, null Tsuhan Chen   +2 more
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Image Retrieval and Relevance Feedback

2009
Relevance feedback is a means for refining a query in an information retrieval system by asking the user to specify how relevant each result of the query is. An image retrieval session relying on relevance feedback is interactive and iterative. The session is divided into several consecutive rounds; at every round, the user provides feedback regarding ...
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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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Learning by examples as relevance feedback and relevance feedback as learning by examples

IEE Two-day Seminar. Searching for Information: Artificial Intelligence and Information Retrieval Approaches, 1999
The relevance feedback in information retrieval (IR) is used for query formulation and consists of assessing a sample of retrieved documents. Probably, the most known probabilistic model based on learning from relevance feedback is the so called Robertson and Sparck-Jones' model (RSJ) (S. Robertson and K. Sparck-Jones, 1976; C.
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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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