Hyper Graph Attribute Model for Web Image Search

Abstract

Image search reranking is an effective approach to refine the text-based image search result. Text-based image retrieval suffers from essential difficulties that are caused mainly by the incapability of the associated text to appropriately describe the image content. In this paper, reranking methods are suggested address this problem in scalable fashion. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph can be used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. It improves the performance over the text-based image search engine.

Key Words: hypergraph; attribute-assisted