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International audienceText-based image retrieval is a popular and simple framework consisting in using text annotations (e.g. image names, tags) to perform image retrieval, allowing to handle efficiently very large image collections. Even if the set of images retrieved using text annotations is noisy, it constitutes a reasonable initial set of images that can be considered as a bootstrap and improved further by analyzing image content. In this context, this paper introduces an approach for improving this initial set by re-ranking the so-obtained images, assuming that non-relevant images are scattered (i.e. they do not form clusters), unlike the relevant ones. More specifically, the approach consists in computing efficiently and on the fly frequent closed patterns, and in re-ranking images based on the number of patterns they contain. To do this, the paper introduces a simple but powerful new scoring function. The approach is validated on three different datasets for which state-of-the-art results are obtained

Image re-ranking based on statistics of frequent patterns