Results 251 to 260 of about 12,314 (276)

Weighting visual features with pseudo relevance feedback for CBIR

Proceedings of the ACM International Conference on Image and Video Retrieval, 2010
This paper proposes an automatic visual feature weighting method to enhance content-based image retrieval (CBIR). In particular, the proposed method is able to capture user's search intention by identifying the important visual features located at region of interest.
Jian Chen, Rui Ma, Zhong Su
openaire   +1 more source

Selecting good expansion terms for pseudo-relevance feedback

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, 2008
Pseudo-relevance feedback assumes that most frequent terms in the pseudo-feedback documents are useful for the retrieval. In this study, we re-examine this assumption and show that it does not hold in reality - many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval.
Guihong Cao, Jian-Yun Nie, Jianfeng Gao 0001, Stephen Robertson   +3 more
openaire   +1 more source

Iterative Estimation of Document Relevance Score for Pseudo-Relevance Feedback

2017
Pseudo-relevance feedback (PRF) is an effective technique for improving the retrieval performance through updating the query model using the top retrieved documents. Previous work shows that estimating the effectiveness of feedback documents can substantially affect the PRF performance. Following the recent studies on theoretical analysis of PRF models,
Mozhdeh Ariannezhad, Ali Montazeralghaem, Hamed Zamani, Azadeh Shakery   +3 more
openaire   +1 more source

Pseudo-Relevance Feedback for Multimedia Retrieval

2003
Video information retrieval requires a system to find information rele-vant to a query which may be presented simultaneously in different ways through a text description, audio, still images and/or video sequences. The actual search also takes place in the text, audio, image or video domain.
Rong Yan, Alexander G. Hauptmann, Rong Jin   +2 more
openaire   +1 more source

Theoretical Analysis of Interdependent Constraints in Pseudo-Relevance Feedback

The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018
Axiomatic analysis is a well-defined theoretical framework for analytical evaluation of information retrieval models. The current studies in axiomatic analysis implicitly assume that the constraints (axioms) are independent. In this paper, we revisit this assumption and hypothesize that there might be interdependence relationships between the existing ...
Ali Montazeralghaem, Hamed Zamani, Azadeh Shakery   +2 more
openaire   +1 more source

Effective pseudo-relevance feedback for spoken document retrieval

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
With the exponential proliferation of multimedia associated with spoken documents, research on spoken document retrieval (SDR) has emerged and attracted much attention in the past two decades. Apart from much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the ...
Yi-Wen Chen, Kuan-Yu Chen 0002, Hsin-Min Wang, Berlin Chen   +3 more
openaire   +1 more source

Learning-Based Pseudo-Relevance Feedback for Patent Retrieval

2012
Pseudo-relevance feedback (PRF) is an effective approach in Information Retrieval but unfortunately many experiments have shown that PRF is ineffective in patent retrieval. This is because the quality of initial results in the patent retrieval is poor and therefore estimating a relevance model via PRF often hurts the retrieval performance due to off ...
Parvaz Mahdabi, Fabio Crestani
openaire   +1 more source

Utilizing Pseudo-Relevance Feedback in Fusion-based Retrieval

Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval, 2018
The usage of positive relevance feedback in fusion-based retrieval was previously shown to be very useful. Yet, in many retrieval use-cases, no actual relevance feedback may be available. With the absence of relevance data, pseudo-relevance feedback models have been suggested as an alternative.
openaire   +1 more source

Block-based pseudo-relevance feedback for image retrieval

Journal of Experimental and Theoretical Artificial Intelligence, 2022
Wei-Chao Lin
exaly  

SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNet

Knowledge-Based Systems, 2023
Min Pan, Teng Li, Jimmy Xiangji Huang
exaly