Unsupervised Object Discovery from Images by Mining Local Features Using Hashing

  • Gibran Fuentes Pineda
  • Hisashi Koga
  • Toshinori Watanabe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


In this paper, we propose a new methodology for efficiently discovering objects from images without supervision. The basic idea is to search for frequent patterns of closely located features in a set of images and consider a frequent pattern as a meaningful object class. We develop a system for discovering objects from segmented images. This system is implemented by hashing only. We present experimental results to demonstrate the robustness and applicability of our approach.


Hash Function Frequent Pattern Object Class Mining Local Locate Component 
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.
    Bar-Hillel, A., Weinshall, D.: Efficient learning of relational object class models. International Journal of Computer Vision 77(1–3), 175–198 (2008)CrossRefGoogle Scholar
  2. 2.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  3. 3.
    Opelt, A., Pinz, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(3), 416–431 (2006)CrossRefGoogle Scholar
  4. 4.
    Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. International Journal of Computer Vision 74(2), 167–181 (2007)CrossRefGoogle Scholar
  5. 5.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: Thirtieth Annual ACM Symposium on the Theory of Computing, pp. 604–613 (1998)Google Scholar
  6. 6.
    Haveliwala, T.H., Gionis, A., Indyk, P.: Scalable techniques for clustering the web. In: Third International Workshop on the Web and Databases, pp. 129–134 (2000)Google Scholar
  7. 7.
    Tsunoda, N., Watanabe, T., Sugawara, K.: Image segmentation by adaptive thresholding of minimum spanning trees. Transactions of the Institute of Electronics, Information and Communication Engineers J87-D-II(2), 586–594 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gibran Fuentes Pineda
    • 1
  • Hisashi Koga
    • 1
  • Toshinori Watanabe
    • 1
  1. 1.Graduate School of Information SystemsThe University of Electro-CommunicationsTokyoJapan

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