Large scale image search with geometric coding

  • Authors:

  • Wengang Zhou;Houqiang Li;Yijuan Lu;Qi Tian

  • Affiliations:

  • University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China;Texas State University, San Marcos, TX, USA;University of Texas at San Antonio, San Antonio, China

  • Venue:
  • MM '11 Proceedings of the 19th ACM international conference on Multimedia
  • Year:
  • 2011

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Abstract

Bag-of-Visual-Words model is popular in large-scale image search. However, traditional Bag-of-Visual-Words model does not capture the geometric context among local features in images. To fully explore geometric context of all visual words in images, efficient global geometric verification methods are demanded. In this paper, we propose a novel geometric coding algorithm to encode the spatial context among local features of an image for large scale partial duplicate image retrieval. Our approach is not only computationally efficient, but also can effectively detect duplicate images with rotation, scale changes, occlusion, and background clutter with low computational cost. Experiments show the promising results of our approach.