NDLI: Learning Robust Similarity Measures for 3D Partial Shape Retrieval

Content Provider	Springer Nature Link
Author	Liu, Yi Wang, Xu Lei Wang, Hua Yan Zha, Hongbin Qin, Hong
Copyright Year	2009
Abstract	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.
Starting Page	408
Ending Page	431
Page Count	24
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	89
Issue Number	2-3
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2009-10-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Partial similarity measure 3D shape retrieval Earth mover’s distance Adaboost Pattern Recognition Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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