Title: A new CBIR approach based on relevance feedback and optimum-path forest classification
Authors: da Silva, André Tavares
Xavier, Alexandre
Magalhães, Léo Pini
Citation: Journal of WSCG. 2010, vol. 18, no. 1-3, p. 73-80.
Issue Date: 2010
Publisher: Václav Skala - UNION Agency
Document type: článek
URI: http://wscg.zcu.cz/WSCG2010/Papers_2010/!_2010_J_WSCG-2010_1-3.pdf
ISBN: 978-80-86943-89-3
ISSN: 1213–6972 (hardcover)
1213–6980 (CD-ROM)
1213–6964 (online)
Keywords: relevanční zpětná vazba;obsahové vyhledávání obrazů
Keywords in different language: relevance feedback;content based image retrieval
Abstract: Recently some CBIR approaches have shown the use of relevance feedback to train a pattern classifier to select relevant images for retrieval. This paper revisits this strategy by using an optimum-path forest (OPF) classifier. During relevance feedback iterations, the proposed method uses the OPF classifier to decide which database images are relevant or not. Images classified as relevant are sorted and presented to the user for a new iteration. Such images are ordered according to the normalized distance using relevant and irrelevant representative images, computed previously by the OPF classifier. Our experiments show that the proposed approach requires fewer iterations, being faster and more effective than methods based on SVM.
Rights: © Václav Skala - UNION Agency
Appears in Collections:Number 1-3 (2010)

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/1270

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