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Unsupervised similarity learning through Cartesian product of ranking references
2017
Pattern Recognition Letters
Despite the consistent advances in visual features and other Multimedia Information Retrieval (MIR) techniques, measuring the similarity among multimedia objects is still a challenging task for an effective retrieval. In this scenario, similarity learning approaches capable of improving the effectiveness of retrieval in an unsupervised way are indispensable. A novel method, called Cartesian Product of Ranking References (CPRR), is proposed with this objective in this paper. The proposed method
doi:10.1016/j.patrec.2017.10.013
fatcat:q2gbx7ofgvatrjyxriwtfz7t4y