Evaluation of Global Descriptors for Large Scale Image Retrieval

  • Hai Wang
  • Shuwu Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

In this paper, we evaluate the effectiveness and efficiency of the global image descriptors and their distance metric functions in the domain of object recognition and near duplicate detection. Recently, the global descriptor GIST has been compared with the bag-of-words local image representation, and has achieved satisfying results. We compare different global descriptors in two famous datasets against mean average precision (MAP) measure. The results show that Fuzzy Color and Texture Histogram (FCTH) is outperforming GIST and several MPEG-7 descriptors by a large margin. We apply different distance metrics to global features so as to see how the similarity measures can affect the retrieval performance. In order to achieve the goal of lower memory cost and shorter retrieval time, we use the Spectral Hashing algorithm to embed the FCTH in the hamming space. Querying an image, from 1.26 million images database, takes 0.16 second on a common notebook computer without losing much searching accuracy.

Keywords

Discrete Cosine Transform Mean Average Precision Global Descriptor Edge Histogram Descriptor Mean Average Precision Score 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hai Wang
    • 1
  • Shuwu Zhang
    • 1
  1. 1.Institute of Automation Chinese Academy of SciencesChina

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