Author(s)

Xiaohong Liu, Dechao Wu, Yuhang Chen, Panjing Li, Zhijian Qu^*

School of Computer Science and Technology, Shandong University of Technology, Zibo, China.

View-based 3D model retrieval methods are attracted intensive research attentions due to the high expression and stable features. In the paper, the bag-of-words (BOW) standardization based SIFT feature were extracted from three projection views of a 3D model, and then the distributed K-means cluster algorithm based on a Hadoop platform was employed to compute feature vectors and cluster 3D models. In order to get precise initial cluster center, the maximum and minimum principle based Canopy algorithm was also presented. The similarity of models was determined by the distance between the query model and each cluster center, and the cluster which nearest to the query model will be return as retrieval results. The simulations indicated that the proposed method had good results in terms of 3D model retrieval accuracy and retrieval time efficiency.

Map-Reduce, 3D Model Retrieval, K-Means, Feature Extraction

Liu, X. , Wu, D. , Chen, Y. , Li, P. and Qu, Z. (2018) Distributed Cluster Based 3D Model Retrieval with Map-Reduce. Journal of Computer and Communications, 6, 83-93. doi: 10.4236/jcc.2018.65007.