{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T07:13:42Z","timestamp":1763968422245,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.<\/jats:p>","DOI":"10.3390\/info11110499","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T02:34:54Z","timestamp":1603679694000},"page":"499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Video Classification System Using a Block-Based Motion Vector"],"prefix":"10.3390","volume":"11","author":[{"given":"Jayasree","family":"K","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Model Engineering College, Ernakulam, Kerala 682021, India"}]},{"given":"Sumam","family":"Mary Idicula","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Cochin University of Science and Technology, Ernakulam, Kerala 682022, India"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Petkovi\u0107, M., and Jonker, W. (2004). Content-Based Video Retrieval, Springer Science and Business Media LLC.","DOI":"10.1007\/978-1-4757-4865-9"},{"key":"ref_2","unstructured":"Ferman, A.M., and Tekalp, A.M. (1999, January 24\u201328). Probabilistic analysis and extraction of video content. Proceedings of the Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), Institute of Electrical and Electronics Engineers (IEEE), Kobe, Japan."},{"key":"ref_3","unstructured":"Yuan, Y., Song, Q.-B., and Shen, J.-Y. (2002, January 4\u20135). Automatic video classification using decision tree method. Proceedings of the International Conference on Machine Learning and Cybernetics, Institute of Electrical and Electronics Engineers (IEEE), Beijing, China."},{"key":"ref_4","unstructured":"Vakkalanka, S. (2005). Multimedia Content Analysis for Video Classification and Indexing. 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