Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2006

Abstract

Motivated by the studies in Gestalt principle, this paper describes a novel approach on the adaptive selection of visual features for trademark retrieval. We consider five kinds of visual saliencies: symmetry, continuity, proximity, parallelism and closure property. The first saliency is based on Zernike moments, while the others are modeled by geometric elements extracted illusively as a whole from a trademark. Given a query trademark, we adaptively determine the features appropriate for retrieval by investigating its visual saliencies. We show that in most cases, either geometric or symmetric features can give us good enough accuracy. To measure the similarity of geometric elements, we propose a maximum weighted bipartite graph (WBG) matching algorithm under transformation sets which is found to be both effective and efficient for retrieval. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Keywords

trademark image retrieval, Gestalt principle, bipartite graph matching under transformation sets

Discipline

Graphics and Human Computer Interfaces | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Pattern Recognition

Volume

39

Issue

5

First Page

988

Last Page

1001

ISSN

0031-3203

Identifier

10.1016/j.patcog.2005.08.012

Publisher

Elsevier

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