3D model semantic classification and retrieval with Gaussian processes

Abstract

A novel 3D model retrieval and semantic classification method using Gaussian processes was 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 AC2using histogram of angle between the centroid and pairs of random points was proposed,which combined D2of shape distribute as low-level feature.The Gaussian processes were used for 3D model semantic classification as supervised learning,and the predictive distribution of the semantic class probability was computed for associating low-level features with query concepts.The method ranked models by dissimilarity measure incorporating the semantic distance and the shape feature distance.Experimental results showed 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.