TY - GEN
T1 - Multiple regression estimation for motion analysis and segmentation
AU - Cherkassky, Vladimir
AU - Ma, Yunqian
AU - Wechsler, Harry
PY - 2004/12/1
Y1 - 2004/12/1
N2 - This paper describes multiple model estimation for motion analysis and segmentation (oka spatial partitioning), from point correspondences in two successive images. In motion analysis applications, available (training) data is generated by several unknown models (motions). However, the correspondence between data samples and different models (motions) is unknown. Hence, the goal of learning (motion estimation) is two-fold, i.e. estimation (learning) of unknown motions (models) and separation (segmentation) of available data into several subsets corresponding to different motions. We present the mathematical formulation for multiple motion estimation, as a problem of learning several (regression) mappings, from a single data set, and then show a constructive (SVM-based) learning algorithm developed for this setting. Experimental results show potential advantages of the proposed method.
AB - This paper describes multiple model estimation for motion analysis and segmentation (oka spatial partitioning), from point correspondences in two successive images. In motion analysis applications, available (training) data is generated by several unknown models (motions). However, the correspondence between data samples and different models (motions) is unknown. Hence, the goal of learning (motion estimation) is two-fold, i.e. estimation (learning) of unknown motions (models) and separation (segmentation) of available data into several subsets corresponding to different motions. We present the mathematical formulation for multiple motion estimation, as a problem of learning several (regression) mappings, from a single data set, and then show a constructive (SVM-based) learning algorithm developed for this setting. Experimental results show potential advantages of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=10944252005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=10944252005&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2004.1381043
DO - 10.1109/IJCNN.2004.1381043
M3 - Conference contribution
AN - SCOPUS:10944252005
SN - 0780383591
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 2547
EP - 2552
BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -