3D Radon Transform for Shape Retrieval Using
Bag-of-Visual-Features
Jinlin Ma and Ziping Ma
College of Computer Science and Engineering, North Minzu
University, China
Abstract: 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.
Keywords: 3D radon transform,
Bag-of-Visual-Features, 3D models retrieval, K-means algorithm.
Received
March 6, 2018; accepted January 28, 2020
https://doi.org/10.34028/iajit/17/4/5