An Attribute-Assisted Reranking Model for Web Image Search
Keywords:
Text base query, Attribute-assisted, Image retrieval, Query image, hyper graph learning, Image rerankingAbstract
Image search reranking is a successful approach to refine the text-based image search result. Most existing reranking ways are based on low level visual features. This paper proposes to make use of semantic attributes for image search reranking. Depend on the classifiers for all the predefined attributes, each image is represented by an attribute feature containing the responses from these classifiers. A hypergraph is then used to model the relationship between images by combining low level visual features and attribute features. Hypergraph ranking is then performed to order the images. The basic principle is that visually similar images should have similar ranking scores. In this paper, we propose a visual attribute joint hypergraph learning approach at the same time to explore two information sources. A hypergraph is created to model the relationship of all images. We conduct experiments on more than 1,000 queries in MSRA-MMV2.0 data set. The experimental results indicate the productiveness of our approach.
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