Attribute-assisted reranking for web image retrieval

Abstract

Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the pre-defined attributes, each image is represented by a attribute feature consisting of the responses from these classifiers. A hypergraph is then used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. We conduct experiments on 300 queries in MSRA-MM V2.0 dataset. The experimental results demonstrate the effectiveness of our approach. © 2012 ACM.