关键词: 三维模型分类, Markov模型, 统计特征量, 相似度, 准确率

Abstract: This paper proposes a new method of 3D object classification based on statistical feature and Markov model.It spatially divides the 3D model of objects into different parts and then extracts statistical feature from each part.The different parts in spatial domain are observed as a certain sequence，pseudo time series of Markov model composed of different parts statistical features are acquired.The model parameters are learned for different class from the training data.In recognition，the output class label of a 3D model is the one that maximizes the joint likelihood.The experiment achieves better results in most cases with accuracy of 90%.

Key words: 3D model classification, Markov model, statistical features, similarity, accuracy