TY - GEN
T1 - 3D head model classification using KCDA
AU - Ma, Bo
AU - Qu, Hui Yang
AU - Wong, Hau San
AU - Lu, Yao
PY - 2006
Y1 - 2006
N2 - In this paper, the 3D head model classification problem is addressed by use of a newly developed subspace analysis method: kernel clustering-based discriminant analysis or KCDA as an abbreviation. This method works by first mapping the original data into another high-dimensional space, and then performing clustering-based discriminant analysis in the feature space. The main idea of clustering-based discriminant analysis is to overcome the Gaussian assumption limitation of the traditional linear discriminant analysis by using a new criterion that takes into account the multiple cluster structure possibly embedded within some classes. As a result, Kernel CDA tries to get through the limitations of both Gaussian assumption and linearity facing the traditional linear discriminant analysis simultaneously. A novel application of this method in 3D head model classification is presented in this paper. A group of tests of our method on 3D head model dataset have been carried out, reporting very promising experimental results.
AB - In this paper, the 3D head model classification problem is addressed by use of a newly developed subspace analysis method: kernel clustering-based discriminant analysis or KCDA as an abbreviation. This method works by first mapping the original data into another high-dimensional space, and then performing clustering-based discriminant analysis in the feature space. The main idea of clustering-based discriminant analysis is to overcome the Gaussian assumption limitation of the traditional linear discriminant analysis by using a new criterion that takes into account the multiple cluster structure possibly embedded within some classes. As a result, Kernel CDA tries to get through the limitations of both Gaussian assumption and linearity facing the traditional linear discriminant analysis simultaneously. A novel application of this method in 3D head model classification is presented in this paper. A group of tests of our method on 3D head model dataset have been carried out, reporting very promising experimental results.
KW - 3D head model classification
KW - Clustering-based Discriminant Analysis (CDA)
KW - Kernel Clustering-based Discriminant Analysis (KCDA)
KW - Kernel Fuzzy c-means
KW - Kernel Linear Discriminant Analysis (KLDA)
UR - http://www.scopus.com/inward/record.url?scp=33845245932&partnerID=8YFLogxK
U2 - 10.1007/11922162_114
DO - 10.1007/11922162_114
M3 - Conference contribution
AN - SCOPUS:33845245932
SN - 3540487662
SN - 9783540487661
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1008
EP - 1017
BT - Advances in Multimedia Information Processing - PCM 2006
PB - Springer Verlag
T2 - PCM 2006: 7th Pacific Rim Conference on Multimedia
Y2 - 2 November 2006 through 4 November 2006
ER -