Title :
Hierarchical overlapped growing neural gas networks with applications to video shot detection and motion characterization
Author :
Cao, Xiang ; Suganthan, P.N.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
fDate :
6/24/1905 12:00:00 AM
Abstract :
This paper describes a hierarchical overlapped architecture (HOGNG) based upon the growing neural gas (GNG) network. The proposed architecture combines the unsupervised and supervised learning schemes in GNG. This novel network model was used to perform automatic video shot detection and motion characterization. Experimental results are presented to show the good classification accuracy of the proposed algorithm on real MPEG video sequences
Keywords :
image sequences; learning (artificial intelligence); motion estimation; neural nets; video signal processing; HOGNG; hierarchical overlapped growing neural gas networks; motion characterization; real MPEG video sequences; supervised learning; unsupervised learning; video shot detection; Cameras; Gunshot detection systems; Material storage; Motion detection; Network topology; Neural networks; Supervised learning; Transform coding; Video compression; Video sequences;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007642
