Video Classification using Thepade’s Sorted Block Truncation Coding using Bayes, Function, Lazy, Rule and Tree Classifiers
Madhura M. Kalbhor1, Sudeep D. Thepade2, Sanjay R. Sange3
1Madhura M. Kalbhor, Department of Computer Engineering, PCCOE, Savitribai Phule Pune University, Pune (Maharashtra), India.
2Sudeep D. Thepade, Department of Computer Engineering, PCCOE, Savitribai Phule Pune University, Pune (Maharashtra), India.
3Sanjay R. Sange, Department of Information Technology, MPSTME, NMIMS University Mumbai (Maharashtra), India.
Manuscript received on 15 June 2015 | Revised Manuscript received on 25 June 2015 | Manuscript Published on 30 June 2015 | PP: 130-133 | Volume-4 Issue-5, June 2015 | Retrieval Number: E4067064515/15©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Video classification is a process of grouping the relevant videos under the predefined set of categories. With the advance technology there is significant growth of video data. To properly manage this data there is need of efficient system. To store the video data in an efficient manner, video classification plays a vital role. This paper proposes a video classification system that uses Thepade’s sorted block truncation coding method to fetch the attributes from the videos. The fetched attributes are supplied to twelve different classifiers belonging to Bayes, Function, Lazy, Rule and Tree classifier families. With the proposed classification system Simple Logistic classifier have given the best classification accuracy of 89.83%.
Keywords: Content Based Video Classification, Thepades Sorted Block Truncation Coding, Data Mining Classifiers.
Scope of the Article: Classification