Video shot boundary detection using RBFNN minimizing the L-GEM

Author

Huang, Zheng-wei ; Ng, Wing W Y ; Chan, Patrick P K ; Li, Jincheng ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

4

fYear

2010

fDate

11-14 July 2010

Firstpage

2156

Lastpage

2160

Abstract

Shot boundary detection (SBD) is the key step of key frame extraction for Content-Based Video Retrieval (CBVR). In this paper, we propose a shot boundary detection method by Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Frame differences are classified as either boundary or non-boundary by the RBFNN. The statistical features of DC image extracted from each frame are used as the input features describing the frame difference. The proposed SBD method is compared with an existing method using News videos. Experimental results show that the proposed method is effective.

Keywords

content-based retrieval; feature extraction; object detection; radial basis function networks; video retrieval; video signal processing; DC image extraction; content-based video retrieval; key frame extraction; localized generalization error frame differences; news videos; radial basis function neural network; video shot boundary detection; Cybernetics; Feature extraction; Machine learning; Neurons; Pattern recognition; Pixel; Training; L-GEM; RBFNN; Shot boundary detection (SBD);

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5580487

Filename

5580487