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Text-based image retrieval is a popular and simple framework, which consists in using text annotations (e.g., image names, tags) to efficiently collect images relevant to a query word, from 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 closed frequent 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. Moreover, after the re-ranking process, we show how pattern mining techniques can also be applied for promoting diversity in the top-ranked images. The approach is validated on three different datasets for which state-of-the-art results are obtained.

Image re-ranking system based on closed frequent patterns