Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Texto completo
Autor(es):
Guimaraes Pedronette, Daniel Carlos [1] ; Calumby, Rodrigo T. [2, 3] ; Torres, Ricardo da S. [2]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] State Univ Sao Paulo UNESP, Dept Stat Appl Math & Comp, Rio Claro, SP - Brazil
[2] Univ Estadual Campinas, UNICAMP, Inst Comp, Recod Lab, Campinas, SP - Brazil
[3] Univ Feira de Santana UEFS, Dept Exact Sci, Feira De Santana, BA - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING; AUG 10 2015.
Citações Web of Science: 5
Resumo

The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks. (AU)

Processo FAPESP: 13/08645-0 - Reclassificação e agregação de listas para tarefas de recuperação de imagens
Beneficiário:Daniel Carlos Guimarães Pedronette
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores
Processo FAPESP: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Beneficiário:Ricardo da Silva Torres
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE