Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
An Experimental Comparison of Feature-Based 3D Retrieval Methods
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Content-based retrieval of 3D models
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Graphical Models
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
International Journal of Computer Vision
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
Visual Similarity Based 3D Shape Retrieval Using Bag-of-Features
SMI '10 Proceedings of the 2010 Shape Modeling International Conference
| Hi-index | 0.00 |
Recent investigations illustrate that view-based methods, with pose normalization pre-processing get better performances in retrieving rigid models than other approaches and still the most popular and practical methods in the field of 3D shape retrieval [9,10,11,12]. In this paper we present an improvement of the BF-SIFT method proposed by Ohbuchi et al [1]. This method is based on bag-of-features to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform [2]) algorithm into a histogram using vector quantization which is based on a global visual codebook. In order to improve the retrieval performances, we propose to associate to each 3D object its local visual codebook instead of a unique global codebook. The experimental results obtained on the Princeton Shape Benchmark database [3] show that the proposed method performs better than the original method.