Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 61, Issue 4, Pages 2088-2098Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2013.2262760
Keywords
Distance metric learning; Hausdorff distance; object search; view pair selection
Categories
Funding
- National 973 Program of China [2013CB329604]
- National 863 Program of China [2012AA011005]
- National Natural Science Foundation of China [6127239, 61229301]
- Program for New Century Excellent Talents in University [NCET-12-0836]
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In view-based 3-D object retrieval, each object is described by a set of views. Group matching thus plays an important role. Previous research efforts have shown the effectiveness of Hausdorff distance in group matching. In this paper, we propose a 3-D object retrieval scheme with Hausdorff distance learning. In our approach, relevance feedback information is employed to select positive and negative view pairs with a probabilistic strategy, and a view-level Mahalanobis distance metric is learned. This Mahalanobis distance metric is adopted in estimating the Hausdorff distances between objects, based on which the objects in the 3-D database are ranked. We conduct experiments on three testing data sets, and the results demonstrate that the proposed Hausdorff learning approach can improve 3-D object retrieval performance.
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