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...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Ruofei Zhang, Zhongfei (Mark) Zhang
• Keywords: Asymmetric Sample Distributions, Computer Vision, ECCV 2004, Overall Retrieval Semantics, Relevance Feedback Data, Relevance Feedback Method, User Relevance Feedback