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.