A 3D model recognition mechanism based on deep Boltzmann machines

The effectiveness of 3D model recognition generally depends on the feature representations and classification methods. Previous algorithms have not shown good capacities to detect 3D model[U+05F3]s feature, thus, they seem not to be competent to recognize 3D model. Meanwhile, recent efforts have illustrated that Deep Boltzmann Machines (DBM) have great power to approximate the distributions of input data, and can archive state-of-the-arts results. In this paper, we propose a novel 3D model recognition mechanism based on DBM, which can be divided into two parts: one is feature detecting based on DBM, and the other is classification based on semi-supervised learning method. During the first part, the high-level abstraction representation can be obtained from a well-trained DBM, and the feature is used in semi-supervised classification method in the second part. The experiments are conducted on publicly available 3D model data sets: Princeton Shape Benchmark (PSB), SHREC[U+05F3]09 and National Taiwan University (NTU). The proposed method is compared with several state-of-the-art methods in terms of several popular evaluation criteria, and the experimental results show better performance of the proposed model.