Shot boundary detection based on supervised locality preserving projections and KNN-SVM classifier

Authors:
Yongliang Xiao;Limin Xia
Affiliations:
School of Infonnation Science and Engineering, Central South University, Changsha, China and Department of Information Management, Hunan College of Finance and Economics, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China
Venue:
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Year:
2010

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Abstract

We introduce a novel method to detect video shot boundary. The method includes two stages: video frame feature supervised extraction and video frame supervised classification. Firstly, we use a supervised locality preserving projections to extract video feature, which can enlarger the difference between two shots. Then we create two cascaded KNN-SVM classifier which combines the ideas of SVM and K nearest neighbor to classify video frame to abrupt transitions, gradual transitions or normal frames. Experimental results show the effectiveness of our method.