Weighting visual features with pseudo relevance feedback for CBIR

Abstract

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. Given a query image, the importances of visual features are automatically weighted by a random walk algorithm from a feature association graph, whose association strength is estimated by a localized visual word co-occurrence count among a set of pseudo relevance feedbacks. The visual word here is defined with a bag-of-features model whose visual feature vocabulary is generated by a k-means clustering algorithm. For quantitative evaluation, we implement a prototype CBIR system with weighted visual features (WVF). Extensive experiments on CalTech-101 dataset demonstrate the efficiency and effectiveness of WVF for CBIR. Copyright © 2010 ACM.