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Image Retrieval Based on Statistical and Geometry Features

  • Yu Liu
  • Liangbing Feng
  • Xing Wang
  • Ning Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)

Abstract

The most popular approach to large scale image retrieval is based on the bag-of-word (BoW) representation of images. There is an important trick how the statistical and geometry features of BoW are efficiently used. We present a two-step approach for image retrieval with statistical features and spatial geometry information been considered in different step. In the first step, the statistical features of the images’ BoW are achieved to capture the underlying image topic to screen those images. In the second step, images from same topic are ranked using the concept of co-occurrence features (a type of geometry features). Computational cost of the retrieval is reduced because the first step does not consider computing the expensive spatial geometry information and the second step only uses significant features to rank images. Experiments on the Oxford 5K benchmark show that the proposed technique can stably achieve nearly the same result compared with a state-of-the-art retrieval method [1] while only spending about a tenth of the time the method takes.

Keywords

Image Retrieval Visual Word Query Image Sift Feature Geometry Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yu Liu
    • 1
  • Liangbing Feng
    • 1
  • Xing Wang
    • 1
  • Ning Guo
    • 1
  1. 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina

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