TY - GEN
T1 - Shape topics
T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
AU - Yi, Liu
AU - Hongbin, Zha
AU - Hong, Qin
PY - 2006
Y1 - 2006
N2 - This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bag-of-words model. The main contributions of this paper are three-fold as follows: 1) a partial shape dissimilarity measure is proposed to rank shapes according to their distances to the input query, without using any time-consuming alignment procedure; 2) by applying the probabilistic text analysis technique, a highly compact representation "Shape Topics" and accompanying algorithms are developed for efficient 3D partial shape retrieval, the mapping from "Shape Topics" to "object categories" is established using multi-class SVMs; and 3) a method for evaluating the performance of partial shape retrieval is proposed and tested. To our best knowledge, very few existing methods are able to perform well online partial shape retrieval for large 3D shape repositories. Our experimental results are expected to validate the efficacy and effectiveness of our novel approach.
AB - This paper develops an efficient new method for 3D partial shape retrieval. First, a Monte Carlo sampling strategy is employed to extract local shape signatures from each 3D model. After vector quantization, these features are represented by using a bag-of-words model. The main contributions of this paper are three-fold as follows: 1) a partial shape dissimilarity measure is proposed to rank shapes according to their distances to the input query, without using any time-consuming alignment procedure; 2) by applying the probabilistic text analysis technique, a highly compact representation "Shape Topics" and accompanying algorithms are developed for efficient 3D partial shape retrieval, the mapping from "Shape Topics" to "object categories" is established using multi-class SVMs; and 3) a method for evaluating the performance of partial shape retrieval is proposed and tested. To our best knowledge, very few existing methods are able to perform well online partial shape retrieval for large 3D shape repositories. Our experimental results are expected to validate the efficacy and effectiveness of our novel approach.
KW - A bag-of-words model
KW - Partial shape retrieval
KW - Probabilistic text analysis
KW - Shape representation
UR - https://www.scopus.com/pages/publications/33845589488
U2 - 10.1109/CVPR.2006.278
DO - 10.1109/CVPR.2006.278
M3 - Conference contribution
SN - 0769525970
SN - 9780769525976
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 2025
EP - 2032
BT - Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Y2 - 17 June 2006 through 22 June 2006
ER -