Point-context descriptor based region search for logo recognition

  • Authors:
  • Jianlong Fu;Jinqiao Wang;Yifan Zhang;Hanqing Lu

  • Affiliations:
  • Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
  • Year:
  • 2012

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Abstract

We propose a novel approach for logo recognition in this paper. Firstly, we adopt a point-context descriptor for region modeling, which is a highly correlated integration of three kinds of features: point, shape, and patch. Secondly, after the query image is segmented into region trees, an asymmetric region-to-image search approach is utilized to visual logo recognition. A weak geometric constraint based on regions is encoded into the inverted file structures to accelerate the search speed. Then we apply global features to refine the results in the re-ranking stage. Finally, we combine each region score both in max-response mode and accumulate-response mode to obtain the final results. To evaluate the performance, we test the proposed approach both on our challenging logo dataset and the Flickr_Logos dataset. Experiments and comparisons show that our approach is superior to the state-of-the-art approaches.