원문정보
초록
영어
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 [1, 2, 3, 4, 5]. In this paper we present an improvement of 3D shape retrieval methods based on bag-of features approach. These methods use this approach to integrate a set of features extracted from 2D views of the 3D objects using the SIFT (Scale Invariant Feature Transform [6]) algorithm into histograms 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 [6], for the BF-SIFT method proposed by Ohbuchi, et al., [2] and CM-BOF proposed by Zhouhui, et al., [3], show that the proposed approach performs better than the original approach.
목차
1. Introduction
2. Presentation of BF-SIFT and CM-BOF Methods
2.1 The BF-SIFT Method
2.2 CM-BOF Method
3. Improvements based on Local Codebooks
4. Experiments and Results
5. Conclusion and Perspectives
Acknowledgements
References