3D Radon Transform for Shape Retrieval Using Bag-of-Visual-Features

In order to improve the accuracy and efficiency of extracting features for 3D models retrieval, a novel approach using 3D radon transform and Bag-of-Visual-Features is proposed in this paper. Firstly the 3D radon transform is employed to obtain a view image using the different features in different angels. Then a set of local descriptor vectors are extracted by the SURF algorithm from the local features of the view. The similarity distance between geometrical transformed models is evaluated by using K-means algorithm to verify the geometric invariance of the proposed method. The numerical experiments are conducted to evaluate the retrieval efficiency compared to other typical methods. The experimental results show that the change of parameters has small effect on the retrieval performance of the proposed method