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

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1069

Lastpage

1074

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007642

Filename

1007642