Various researches have been performed with video abstraction with the constant development of multimedia technology. However, there are some deficiencies that have been encountered in the pre-processing of video frames before attaining classified video archives. To overcome the drawbacks in pre-processing, feature extraction and classification approaches are considered. Here, video indexing has been anticipated with several features’ extraction with dominant frame generation for the input video frame. Fuzzy-based SVM classifier is utilized to categorize frame set into dominant structures. Multi-dimensional Histogram of Oriented Gradients (HOG) and colour feature extraction are used to extract texture features from the video frame. Using the frame sequence, the vector space of structures is captured; dominant frameworks are utilized in video indexing. Shot transitions’ classification is done with a fuzzy system. Experimental outcomes demonstrate that shot boundary detection accuracy increases with an increase in iterations. The simulation was carried out in MATLAB environment. This technique attains an accuracy of about 95.4%, the precision of 100%, and the F1 score of 100% and a recall of 100%. The misclassification rate is 4.6%. The proposed method shows better trade-off than the existing techniques.
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Kalirajan, K., Sudha, M.: Moving object detection for video surveillance. Sci. World J. 10, 8 (2015)
Prasad, D.K., Rajan, D., Rachmawati, L., et al.: Video processing from electro-optical sensors for object detection and tracking in a maritime environment: a survey. IEEE Trans. Intell. Transp. Syst. 18(8), 1993–2016 (2017)
Yuan, Y., Xiong, Z., Wang, Q.: An incremental framework for video-based traffic sign detection, tracking, and recognition. IEEE Trans. Intell. Transp. Syst. 18(7), 1918–1929 (2017)
Chandran, R., Raman, N.: A review on video-based techniques for vehicle detection, tracking, and behaviour understanding. Int. J. Adv. Comp. Electr. Eng. 2(5), 7–13 (2017)
Zhang, B., Li, Z., Perina, A., et al.: Adaptive local movement modelling for robust object tracking. IEEE Trans. Circuits Syst. Video Technol. 27(7), 1515–1526 (2017)
Baccarelli E, Cordeschi N, Mei A, et al. Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: Review, challenges, and a case study. IEEE Network; March 2016
Tavoli R, Kozegar E, Shojafar M, et al. Weighted PCA for improving document image retrieval system based on keyword spotting accuracy. 2013 36th international conference on tele communications and signal processing (TSP); 2013
Karthikeyan, K., Anusha, L., Janani, E., et al.: Neural network for a winner take all competition using palm print recognization. Int J Comp Sci Netw Secure (IJCSNS). 15(3), 91 (2015)
Nascimento, J.C., Marques, J.S.: Performance evaluation of object detection algorithms for video surveillance. IEEE Trans. Multimedia 8(4), 761–774 (2006)
Radhakrishnan, M., Kuttiannan, T., Tiruchengode, N.: Comparative analysis of feature extraction methods for the classification of prostate cancer from TRUS medical images. IJCSI Int J Comput Sci. 9, 1 (2012)
Zhang, H., Hu, R., Song, L. A shot boundary detection method based on color feature. In: Proceedings of the international conference on computer science and network technology (ICCSNT’11), pp. 2541–2544, Harbin, China, December (2011)
Lu, Z.-M., Shi, Y.: Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans. Image Process. 22(12), 5136–5145 (2013)
O. K¨uc¸¨uktunc¸, U. G¨ud¨ukbay, and ¨ O. Ulusoy, Fuzzy color histogram-based video segmentation, Computer Vision and Image Understanding, vol. 114, no. 1, pp. 125–134, 2010
A. F. Smeaton, P.Over, and. R.Doherty, “Video shot boundary detection: seven years of TRECVid activity,” Computer Vision and Image Understanding, vol. 114, no. 4, pp. 411–418, 2010
J. Baber, N. Afzulpurkar, and S. Satoh, “A framework for video segmentation using global and local features,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 27,no. 5, Article ID 1355007, (2013)
G. Pal, D. Rudrapaul, S. Acharjee, R. Ray, S. Chakraborty, and N. Dey, “Video shot boundary detection: a review,” in Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2, S.C. Satapathy, A. Govardhan, K. S. Raju, and J. K. Mandal, Eds., vol. 338 of Advances in Intelligent Systems and Computing, pp. 119–127, Springer, 2015
Zhu, Yingying: SVM-based audio classification for content- based multimedia retrieval. Springer-Verlag, Berlin Heidelberg (2007)
Korytkowski, Marcin: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. Springer-Verlag, Berlin Heidelberg (2008)
Markos, Z.: Integrating motion and color for content-based video classification. Int. J. Innov. Res. Comput. Commun. Eng. 2, 4 (2014)
Ali, W.: Multimodal approach for video surveillance indexing and retrieval. Int. J. Comput. Appl. 64, 1 (2013)
Subashini, K.: “Audio-video based classification using SVM and AANN. Int. J. Comput. Appl. 44(6), 0975–8887 (2012)
Kumar, A.: “Improved fuzzy rule-based classification system using feature selection and bagging for large datasets. Int. J. Sci. Res. 1, 2319–7064 (2015)
Ansari, A., Vasishtha, H.: Content-based video retrieval systems performance based on multiple features and multiple frames using SVM. Int. J. Adv. Comput. Sci. Appl. 7, 8 (2016)
Zampoglou, M., Papadimitriou, T., H., Diamantaras, K., I. Support vector machines content-based video retrieval based solely on motion information. In: Proc. 17th Int. Workshop on Machine Learning for Signal Processing (MLSP-2007), IEEE, August, Thessaloniki, Greece, pp. 176–180 (2007)
Fan, J., Luo, H., Gao, Y. Jain, R. Incorporating concept ontology for hierarchical video classification, Annotation, and visualization. In: IEEE Transactions on Multimedia, Vol. 9, No. 5, pp. 939–957 (2007)
Ansari, A., Vasishtha, H. CBVR and Classification of Videos based on BTC Color Features using SVM, In: Proceedings of the international conference on emerging research trends in applied engineering and technology, KGCE, Karjat, ISBN: 978-93-5258-409-3 (2016)
Mukesh, M., Ruchika, L.: Image compression using vector quantization algorithms: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 6 (2013)
Jari, K. Increasing the error tolerance in transmission of vector quantized images by self-organizing map. Helsinki University of Technology, Finland
Weiming, H., Nianhua, X., Li, L., Xianglin, Z., Maybank, S. A survey on visual content-based video indexing and retrieval. In: IEEE transactions on systems, man, and cybernetics, Part C (Applications and Reviews), 41–6,797–819 (2011)
Chen, L.-H., Chin, K.-H., Liao, H.-Y.: An integrated approach to video retrieval. Proc. Nineteenth Conf. Aust. Database 75, 49–55 (2008)
Shruthi, N., Priyamvada, S. Dominant frame extraction for video indexing. Int. Conf. Recent Trends Electr. Inf. Commun. Technol. (2017)
This article has been written with the financial support of RUSA–Phase 2.0.
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Gayathri, N., Mahesh, K. Improved Fuzzy-Based SVM Classification System Using Feature Extraction for Video Indexing and Retrieval. Int. J. Fuzzy Syst. 22, 1716–1729 (2020). https://doi.org/10.1007/s40815-020-00884-z
- Multimedia technology
- Video indexing
- Feature extraction
- Fuzzy-based SVM classifier
- Multi-dimensional Histogram of Oriented Gradients (HOG) and Colour Feature Extraction