Unsupervised distance learning by rank correlation measures for image retrieval
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Data
2015-06-22
Autores
Okada, César Yugo [UNESP]
Pedronette, Daniel Carlos Guimarães [UNESP]
Da Torres, Ricardo S.
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Resumo
Ranking accurately collection images is the main objective of Content-based Image Retrieval (CBIR) systems. In fact, the set of images ranked at the first positions generally defines the effectiveness of provided search services, i.e., they are used for assessing automatically the quality of search systems as this set usually contains the collection images that are of interest. Recently, the use of ranking information (e.g., rank correlation) has been used in different research initiatives with the objective of improving the effectiveness of image retrieval tasks. This paper presents a broad rank correlation analysis for unsupervised distance learning on image retrieval tasks. Various well-known rank correlation measures are considered and two new measures are proposed. Several experiments were conducted considering various image datasets involving shape, color, and texture descriptors. Experimental results demonstrate that ranking information can be exploited for distance learning tasks successfully. Evaluated approaches yield better results in terms of effectiveness than various state-of-the-art algorithms.
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Content-based image retrieval, Measures, Rank correlation
Como citar
ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval, p. 331-338.