Neural network based temporal video segmentation.
The organization of video information in video databases requires automatic temporal segmentation with minimal user interaction. As neural networks are capable of learning the characteristics of various video segments and clustering them accordingly, in this paper, a neural network based technique is developed to segment the video sequence into shots automatically and with a minimum number of user-defined parameters. We propose to employ growing neural gas (GNG) networks and integrate multiple frame difference features to efficiently detect shot boundaries in the video. Experimental results are presented to illustrate the good performance of the proposed scheme on real video sequences.
Cao
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School of Electrical & Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore. gonnacx@pmail.ntu.edu.sg
Suganthan
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PN
D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing / Q000379:methods / Q000639:trends; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013997:Time Factors; D014741:Video Recording / Q000295:instrumentation / Q000379:methods