8 March 2018 Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison
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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 1061115 (2018) https://doi.org/10.1117/12.2283516
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qila Sa, Qila Sa, Zhihui Wang, Zhihui Wang, } "Automatic video shot boundary detection using k-means clustering and improved adaptive dual threshold comparison", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1061115 (8 March 2018); doi: 10.1117/12.2283516; https://doi.org/10.1117/12.2283516
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