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)

Abstract

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.

Keywords

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.

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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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