Gestalt-based feature similarity measure in trademark database

• Main Authors: JIANG, Hui, NGO, Chong-wah, TAN, Hung-Khoon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: trademark image retrieval; Gestalt principle; bipartite graph matching under transformation sets; Graphics and Human Computer Interfaces; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6324; https://ink.library.smu.edu.sg/context/sis_research/article/7327/viewcontent/doi_10.1016_j.patcog.2005.08.012.pdf
• Institution: Singapore Management University

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.