Co-transduction for Shape Retrieval View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Xiang Bai , Bo Wang , Xinggang Wang , Wenyu Liu , Zhuowen Tu

ABSTRACT

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. More... »

PAGES

328-341

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008. Improving Shape Retrieval by Learning Graph Transduction in COMPUTER VISION – ECCV 2008
  • 2004. Shape Matching and Recognition – Using Generative Models and Informative Features in COMPUTER VISION - ECCV 2004
  • 2004-10. Scale & Affine Invariant Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007. Analyzing Co-training Style Algorithms in MACHINE LEARNING: ECML 2007
  • 2010. Beyond Pairwise Shape Similarity Analysis in COMPUTER VISION – ACCV 2009
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15557-4
    978-3-642-15558-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15558-1_24

    DOI

    http://dx.doi.org/10.1007/978-3-642-15558-1_24

    DIMENSIONS

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