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

Incorporating multiple distance spaces in optimum-path forest classification to improve feedback-based learning

Full text
da Silva, Andre Tavares [1] ; dos Santos, Jefersson Alex [2] ; Falcao, Alexandre Xavier [2] ; Torres, Ricardo da S. [2] ; Magalhaes, Leo Pini [1]
Total Authors: 5
[1] Univ Campinas Unicamp, Dept Comp Engn & Ind Automat, Sch Elect & Comp Engn, BR-13083852 Campinas, SP - Brazil
[2] Univ Campinas UNICAMP, Inst Comp, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: COMPUTER VISION AND IMAGE UNDERSTANDING; v. 116, n. 4, p. 510-523, APR 2012.
Web of Science Citations: 11

In content-based image retrieval (CBIR) using feedback-based learning, the user marks the relevance of returned images and the system learns how to return more relevant images in a next iteration. In this learning process, image comparison may be based on distinct distance spaces due to multiple visual content representations. This work improves the retrieval process by incorporating multiple distance spaces in a recent method based on optimum-path forest (OPF) classification. For a given training set with relevant and irrelevant images, an optimization algorithm finds the best distance function to compare images as a combination of their distances according to different representations. Two optimization techniques are evaluated: a multi-scale parameter search (MSPS), never used before for CBIR, and a genetic programming (GP) algorithm. The combined distance function is used to project an OPF classifier and to rank images classified as relevant for the next iteration. The ranking process takes into account relevant and irrelevant representatives, previously found by the OPF classifier. Experiments show the advantages in effectiveness of the proposed approach with both optimization techniques over the same approach with single distance space and over another state-of-the-art method based on multiple distance spaces. Crown Copyright (C) 2011 Published by Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 07/52015-0 - Approximation methods for visual computing
Grantee:Jorge Stolfi
Support type: Research Projects - Thematic Grants
FAPESP's process: 09/18438-7 - Large-scale classification and retrieval for complex data
Grantee:Ricardo da Silva Torres
Support type: Regular Research Grants