International Journal of Computer Vision

, Volume 89, Issue 2, pp 408–431

Learning Robust Similarity Measures for 3D Partial Shape Retrieval


    • Key Laboratory of Machine Perception (Ministry of Education)Peking University
    • Chinese Academy of Sciences Key Laboratory of Molecular Developmental BiologyCenter for Molecular Systems Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
  • Xu-Lei Wang
    • Key Laboratory of Machine Perception (Ministry of Education)Peking University
  • Hua-Yan Wang
    • Department of Computer Science and EngineeringHong Kong University of Science and Technology
  • Hongbin Zha
    • Key Laboratory of Machine Perception (Ministry of Education)Peking University
  • Hong Qin
    • Department of Computer ScienceState University of New York at Stony Brook

DOI: 10.1007/s11263-009-0298-x

Cite this article as:
Liu, Y., Wang, X., Wang, H. et al. Int J Comput Vis (2010) 89: 408. doi:10.1007/s11263-009-0298-x


In this paper, we propose a novel approach to learning robust ground distance functions of the Earth Mover’s distance to make it appropriate for quantifying the partial similarity between two feature-sets. First, we define the ground distance as a monotonic transformation of commonly used feature-to-feature base distance (or similarity) measures, so that in computing the Earth Mover’s distance, the algorithm could better turn its focus on the feature pairs that are correctly matched, while being less affected by irrelevant ones. As a result, the proposed method is especially suited for 3D partial shape retrieval where occlusion and clutter are serious problems. We prove that when the transformation satisfies certain conditions, the metric property of the base distance is sufficient to guarantee the ground distance is a metric (and so is the Earth Mover’s distance), which makes fast shape retrieval on large databases technically possible. Second, we propose a discriminative learning framework to optimize the transformation function based on the real Adaboost algorithm. The optimization is performed in the space of the piecewise constant approximations of the transformation without making any parametric assumption. Finally, extensive experiments on 3D partial shape retrieval convincingly demonstrate the effectiveness of the proposed techniques.


Partial similarity measure3D shape retrievalEarth mover’s distanceAdaboost

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© Springer Science+Business Media, LLC 2009