Abstract
Currently Web image search is mostly implemented as text retrieval based on the textual information extracted from the Web page associated with the image. Since the text in the Web page may not match with the image content, image search re-ranking is preferable to refine the text-based search results. In this paper, we propose a novel scheme of latent visual context analysis (LVCA) for image re-ranking. The latent visual context is explored in both latent semantic context and visual link graphs. We argue that the image significance is determined by its contained visual word context, which is analyzed through Latent Semantic Analysis (LSA) and visual word link graph. With the visual word context information, the image context is explored by analysis of image link graph and the significance value for each image can be inferred by VisualRank. In both visual word link graph and image link graph, latent-layer will be incorporated to effectively discover the visual context. We validate our approach on text-query based search results returned by Google Image. Experimental results show improvement of both accuracy and efficiency of our method over the state-of-the-art VisualRank algorithm.
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