3D model retrieve based on K-means clustering

Hui Jing; Meifa Huang; Yanru Zhong

doi:10.1117/12.750672

15 November 2007 3D model retrieve based on K-means clustering

Hui Jing, Meifa Huang, Yanru Zhong

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678823 (2007) https://doi.org/10.1117/12.750672
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

Owing to its fast speed, simple operation, and strong robustness, Shape Distribution is widely used in search engines. This method, however, only considers distances between the objects' shape distribution histograms and ignores the information included. Actually the information of the shape distribution histograms, such as the mean value, the standard deviation, the kurtosis and the skewness, can be used to map the 3D model. As a result, the retrieval precision of Shape Distribution is low. To enhance the retrieve efficiency, a novel method which employs the K-means clustering method is proposed in this paper. First, the models' shape distribution histograms are established by Shape Distribution method and are normalized as the proper format of K-means clustering method. Then, the objects' shape distribution histograms are served as inputs of K-means clustering method and are classified into certain groups by this algorithm. Last, all the models that belong to the classification of the query model are exported as the retrieval results. A case study is used to validate the proposed method. Experimental results show that the retrieval precision by using the proposed method is higher than that of the Shape Distribution method.

Citation Download Citation

Hui Jing, Meifa Huang, and Yanru Zhong "3D model retrieve based on K-means clustering", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678823 (15 November 2007); https://doi.org/10.1117/12.750672

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