Journal ArticleDOI
3-D Object Retrieval With Hausdorff Distance Learning
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TLDR
The results demonstrate that the proposed Hausdorff learning approach can improve 3-D object retrieval performance.Abstract:
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.read more
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