Gestalt-based feature similarity measure in trademark database

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)988-1001
Journal / PublicationPattern Recognition
Volume39
Issue number5
Publication statusPublished - May 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. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Research Area(s)

  • Bipartite graph matching under transformation sets, Gestalt principle, Trademark image retrieval

Citation Format(s)

Gestalt-based feature similarity measure in trademark database. / Jiang, Hui; Ngo, Chong-Wah; Tan, Hung-Khoon.
In: Pattern Recognition, Vol. 39, No. 5, 05.2006, p. 988-1001.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review