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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Priyanka AR, Majumdar J (2015) Video shot detection using texture feature. Int J Sci Res (IJSR)
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern
Wang L, He DC (1990) A new statistical approach for texture analysis. Photogram Eng Remote Sens 56(1):61–66
He D-C, Wang L (1990) Texture unit, texture spectrum, and texture analysis. IEEE Trans Geosci Remote Sens 28(4):509–512
Wang L, He D-C (1990) Texture classification using texture spectrum. Pattern Recogn 23(8):905–910
Majumdar J, Aniketh M, Abhishek B, Hegde N (2017) Video shot detection in transform domain. In: 2017 2nd international conference for convergence in technology (I2CT)
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Sci Mag 315
Patel U, Shah P, Panchal P (2013) Shot detection using pixel-wise difference with adaptive threshold and color histogram method in compressed and uncompressed video. Int J Comput Appl 64(4):0975–8887
Lakshmi Priya GG, Domnic S (2010) Video cut detection using block based histogram differences in RGB color space
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-1632-0_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1631-3
Online ISBN: 978-981-15-1632-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)