Unsupervised similarity learning through cartesian product of ranking references for image retrieval tasks

Registro completo de metadados
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorValem, Lucas Pascotti-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimaraes-
Data de aceite: dc.date.accessioned2021-03-11T00:32:01Z-
Data de disponibilização: dc.date.available2021-03-11T00:32:01Z-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2018-12-11-
Data de envio: dc.date.issued2017-01-10-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI.2016.042-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/169487-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/169487-
Descrição: dc.descriptionDespite 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. 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 uses Cartesian product operations based on rank information for exploiting the underlying structure of datasets. Only subsets of ranked lists are required, demanding low computational efforts. An extensive experimental evaluation was conducted considering various aspects, four public datasets and several image features. Besides effectiveness, experiments were also conducted to assess the efficiency of the method, considering parallel and heterogeneous computing on CPU and GPU devices. The proposed method achieved significant effectiveness gains, including competitive state-of-the-art results on popular benchmarks.-
Formato: dc.format249-256-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016-
Direitos: dc.rightsopenAccess-
Palavras-chave: dc.subjectCartesian product-
Palavras-chave: dc.subjectcontent-based image retrieval-
Palavras-chave: dc.subjecteffectiveness-
Palavras-chave: dc.subjectefficiency-
Palavras-chave: dc.subjectunsupervised learning-
Título: dc.titleUnsupervised similarity learning through cartesian product of ranking references for image retrieval tasks-
Aparece nas coleções:Repositório Institucional - Unesp

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