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)


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.


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.


  1. 1.
    Chang, S.F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. IEEE Transactions on Circuits and Systems for Video Technology 11(6), 688–695 (2001)CrossRefGoogle Scholar
  2. 2.
    Chatzichristofis, S.A., Boutalis, Y.S.: Fcth: Fuzzy color and texture histogram a low level feature for accurate image retrieval. In: 9th International Workshop on Image Analysis for Multimedia Interactive Services, pp. 191–196 (2008)Google Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2) (2008)Google Scholar
  4. 4.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  5. 5.
    Douze, M., Jegou, H., Sandhawalia, H., Amsaleg, L., Schmid, C.: Evaluation of gist descriptors for web-scale image search. In: ACM International Conference on Image and Video Retrieval, pp. 140–147 (2009)Google Scholar
  6. 6.
    Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Lux, M., Chatzichristofis, S.A.: Lire: Lucene image retrieval - an extensible java cbir library. In: 16th ACM International Conference on Multimedia, pp. 1085–1087 (2008)Google Scholar
  9. 9.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)Google Scholar
  10. 10.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Ninth IEEE International Conference On Computer Vision, vol. 2, pp. 1470–1477 (2003)Google Scholar
  12. 12.
    Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: 26th IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  13. 13.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems (2009)Google Scholar

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

Personalised recommendations