Categorizing video shots by utilizing SVM and wavelet

Haina Jiang; Xiquan Xia

doi:10.1117/12.917663

15 November 2011 Categorizing video shots by utilizing SVM and wavelet

Haina Jiang, Xiquan Xia

Proceedings Volume 8335, 2012 International Workshop on Image Processing and Optical Engineering; 83351A (2011) https://doi.org/10.1117/12.917663
Event: 2012 International Workshop on Image Processing and Optical Engineering, 2012, Harbin, China

Abstract

Shots classification plays an important role in well indexing, browsing and retrieving video content. By that, the large amount of video content can be efficiently indexed, and then, it can provide convenience for managing video. In this paper, edge features are firstly extracted by wavelet, which can not only reduce amount of shots data but also preserve the important structural properties of shots. And then, to reflect local properties of shots, ratio of edge pixels in each sub-window is calculated. After that, color moments are computed to reduce loss of global properties, which can assist edge features in well indexing shots. Finally, support vector machine (SVM), which has a very good performance on pattern recognition, is employed to classify shots. Experimental results demonstrate that this method can efficiently categorize video shots and satisfy the basic needs of shots classification.

Citation Download Citation

Haina Jiang and Xiquan Xia "Categorizing video shots by utilizing SVM and wavelet", Proc. SPIE 8335, 2012 International Workshop on Image Processing and Optical Engineering, 83351A (15 November 2011); https://doi.org/10.1117/12.917663

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