Abstract
Video Shot Detection and Summarization plays a vital role in analyzing the contents of the video. The algorithms and methodologies learnt from video shot detection and summarization have a wide range of applications starting from video browsing, content-based video retrieval and storage, surveillance and many more. In an earlier work [1], we have extracted six texture features using gray level co-occurrence matrix, one of the most popular texture feature extraction methods. Frames from input video sequence are converted in texture domain. These video sequences are used to determine the “CUT” transition. In the proposed work, we have used GLCM and texture spectrum to extract texture features from frames in video and used a simple video shot detection method to find “CUT” transition and analyzed the results using quality metric parameters to determine the best feature extractor among GLCM and texture spectrum. The clustering algorithm for video summarization is affinity propagation.
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Acknowledgements
The authors express their sincere gratitude to Prof. N. R. Shetty, Advisor and Dr. H. C. Nagaraj, Principal, Nitte Meenakshi Institute of Technology for giving constant encouragement and support to carry out research at NMIT. The authors extend their thanks and gratitude to the Vision Group on Science and Technology (VGST), Government of Karnataka to acknowledge their research and providing financial support to set up the infrastructure required to carry out the research.
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Majumdar, J., Ashray, M.P., Madhan, H.M., Adiga, D.M. (2020). Video Shot Detection and Summarization Using Features Derived From Texture. In: Gunjan, V., Suganthan, P., Haase, J., Kumar, A., Raman, B. (eds) Cybernetics, Cognition and Machine Learning Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1632-0_16
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DOI: https://doi.org/10.1007/978-981-15-1632-0_16
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