Unsupervised Selective Rank Fusion for Content-based Image Retrieval
The CBIR (Content-Based Image Retrieval) systems are one of the main solutions for image retrieval tasks. These systems are mainly supported by the use of different visual features and machine learning methods. As distinct features produce complementary ranking results with different effectiveness performance, a promising solution consists in combining them. However, how to decide which visual features to combine is a very challenging task, especially when no training data is available. This work proposes three novel methods for selecting and combining ranked lists by estimating their effectiveness in an unsupervised way. The approaches were evaluated in five different image collections and several descriptors, achieving results comparable or superior to the state-of-the-art in most of the scenarios.
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Valem, L. P. and Pedronette, D. C. G. (2019b). Combinação Seletiva Não Supervisionada de Listas Ranqueadas Aplicada a Busca de Imagens pelo Conteúdo. Dissertation (M.Sc. in Computer Science), UNESP (Universidade Estadual Paulista Julio de Mesquita Filho), Rio Claro, São Paulo, Brazil.
Valem, L. P. and Pedronette, D. C. G. (2019c). An unsupervised genetic algorithm framework for rank selection and fusion on image retrieval. In Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR ’19, pages 58-62, New York, NY, USA. ACM.
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Valem, L. P. and Pedronette, D. C. G. (2020b). Unsupervised selective rank fusion for image retrieval tasks. Neurocomputing, 377:182-199.
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