Selection and Combination of Unsupervised Learning Methods for Image Retrieval.
Foundation for Research Support of the State of São Paulo (Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP) - Grants 2017/02091-4 and 2013/08645-0.
Period: 2017-2019 (Concluded)
Advisor: Daniel Carlos Guimarães Pedronette
Abstract:
Despite the consistent advances in visual features and other Content-Based Image Retrieval techniques, measuring the similarity among images is still a challenging task for effective image retrieval. Originally, the Content-Based Image Retrieval (CBIR) systems were solely based on the use of low-level visual features, but evolved through the years in order to incorporate various supervised learning techniques. More recently, unsupervised learning methods have been showing promising results for improving the effectiveness of retrieval results. However, given the development of different methods, a challenging task consists in to exploit the advantages of diverse approaches. As different methods present distinct results even for the same dataset and set of features, a promising approach is to combine these methods. The objective of this research project is to investigate how to combine different methods and features aiming at improving the effectiveness of information retrieval.
Re-Ranking and Rank Aggregation Approaches for Image Retrieval Tasks.
Foundation for Research Support of the State of São Paulo (Fundação de Amparo à Pesquisa do Estado de São Paulo - FAPESP) - Grants 2014/04220-8 and 2013/08645-0.
Period: 2014-2016 (Concluded)
Advisor: Daniel Carlos Guimarães Pedronette
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. Recently, 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.
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