Abstract
Content-Based Image Retrieval (CBIR) systems aims at retrieving the most similar images in a collection by taking into account image visual properties. Users are interested in the images placed at the first positions of the returned ranked lists, which usually are the most relevant ones. Therefore, accurately ranking collection images is of great relevance. However, in general, CBIR approaches perform only pairwise image analysis, that is, they compute similarity (or distance) measures considering only pairs of images, ignoring the rich information encoded in the relationships among images. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. Re-ranking algorithms have been used to exploit contextual information, encoded in the relationships among collection images, while rank aggregation approaches have been used to combine results produced by different image descriptors. In the work developed by the principal investigator during his PhD research, several methods have been proposed for image re-ranking and rank aggregation, aiming at improving the effectiveness of CBIR systems. Experimental results demonstrated the effectiveness of the proposed approaches in comparison with other state-of-the-art methods recently proposed in the literature. However, the relevant results obtained led to new important research challenges. The objective of this research project is to investigate the re-ranking and rank aggregation approaches under various aspects, addressing the challenges still open. Important aspects to be investigated are related to the scalability and efficient computation of image re-ranking algorithms using parallel algorithms on heterogeneous computing environments. Another relevant aspect is the specification and implementation of new re-ranking approaches to be used in different scenarios and applications, such as multimodal and textual retrieval, relevance feedback, and collaborative image retrieval. (AU)
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