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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Guimaraes Pedronette, Daniel Carlos [1] ; Calumby, Rodrigo T. [2, 3] ; Torres, Ricardo da S. [2]
Total Authors: 3
Affiliation:
[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
Total Affiliations: 3
Document type: Journal article
Source: EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING; AUG 10 2015.
Web of Science Citations: 4
Abstract

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)

FAPESP's process: 13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants
FAPESP's process: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Grantee:Ricardo da Silva Torres
Support type: Research Grants - Research Partnership for Technological Innovation - PITE