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ECCV
2004
Springer

Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval

10 years 3 days ago
Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval
This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which c...
Ruofei Zhang, Zhongfei (Mark) Zhang
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Ruofei Zhang, Zhongfei (Mark) Zhang
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