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3D model semantic classification and retrieval with Gaussian processes. (Chinese. English summary) Zbl 1240.68173

Summary: A novel 3D model retrieval and semantic classification method using Gaussian processes is proposed to improve the performance of 3D model retrieval systems, and reduce the ‘semantic gap’ between the shape features and the richness of human semantics. A new type of feature named AC2 using a histogram of angles between the centroid and pairs of random points is proposed, which combines D2 of shape distribute as low-level feature. Gaussian processes are used for 3D model semantic classification as supervised learning, and the predictive distribution of the semantic class probability is computed for associating low-level features with query concepts. The method ranks models by dissimilarity measure incorporating the semantic distance and the shape feature distance. Experimental results show that the multi-class 3D model classification accuracy using the proposed method is significantly higher than those of other supervised learning methods, and the retrieval can capture the query model’s semantics, so the performance is improved.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68P20 Information storage and retrieval of data
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