Gestalt-based feature similarity measure in trademark database

  • Authors:
  • Hui Jiang;Chong-Wah Ngo;Hung-Khoon Tan

  • Affiliations:
  • Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong and Institute for Computational and Mathematical Engineering, Stanford University, USA;Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong

  • Venue:
  • Pattern Recognition
  • Year:
  • 2006

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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.