Abstract
A first step required to allow video indexing and retrieval of visual data is to perform a temporal segmentation, that is, to find the location of camera-shot transitions, which can be either abrupt or gradual. We adopt SVM technique to decide whether a shot transition exists or not within a given video sequence. Active learning strategy is used to accelerate training of SVM-classifiers. We also introduce a new feature description of video frame based on Local Binary Pattern (LBP).Cosine Distance is used to qualify the difference between frames in our works. The proposed method is evaluated on the TRECVID-2005 benchmarking platform and the experimental results reveal the effectiveness of the method.
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Teng, S., Tan, W. (2008). Video Temporal Segmentation Using Support Vector Machine. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_45
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DOI: https://doi.org/10.1007/978-3-540-68636-1_45
Publisher Name: Springer, Berlin, Heidelberg
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