Co-transduction for Shape Retrieval

  • Xiang Bai
  • Bo Wang
  • Xinggang Wang
  • Wenyu Liu
  • Zhuowen Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)


In this paper, we propose a new shape/object retrieval algorithm, co-transduction. The performance of a retrieval system is critically decided by the accuracy of adopted similarity measures (distances or metrics). Different types of measures may focus on different aspects of the objects: e.g. measures computed based on contours and skeletons are often complementary to each other. Our goal is to develop an algorithm to fuse different similarity measures for robust shape retrieval through a semi-supervised learning framework. We name our method co-transduction which is inspired by the co-training algorithm [1]. Given two similarity measures and a query shape, the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re-ranking, and vice-versa. Using co-transduction, we achieved a significantly improved result of 97.72% on the MPEG-7 dataset [2] over the state-of-the-art performances (91% in [3], 93.4% in [4]). Our algorithm is general and it works directly on any given similarity measures/metrics; it is not limited to object shape retrieval and can be applied to other tasks for ranking/retrieval.


Unlabeled Data Image Search Retrieval Rate Query Object Database Object 
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 2010

Authors and Affiliations

  • Xiang Bai
    • 1
  • Bo Wang
    • 1
  • Xinggang Wang
    • 1
  • Wenyu Liu
    • 1
  • Zhuowen Tu
    • 2
  1. 1.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyChina
  2. 2.Lab of Neuro ImagingUniversity of CaliforniaLos Angeles

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