Abstract
Web image re-ranking aims to automatically refine the initial text-based image search results by employing visual information. A strong line of work in image re-ranking relies on building image graphs that requires computing distances between image pairs. In this paper, we present Anchor Concept Graph Distance (ACG Distance), a novel distance measure for image re-ranking. For a given textual query, an Anchor Concept Graph (ACG) is automatically learned from the initial text-based search results. The nodes of the ACG (i.e., anchor concepts) and their correlations well model the semantic structure of the images to be re-ranked. Images are projected to the anchor concepts. The projection vectors undergo a diffusion process over the ACG, and then are used to compute the ACG distance. The ACG distance reduces the semantic gap and better represents distances between images. Experiments on the MSRA-MM and INRIA datasets show that the ACG distance consistently outperforms existing distance measures and significantly improves start-of-the-art methods in image re-ranking.
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