Video Classification using SVM
Ashwini S. Mane1, P. M. Kamde2

1Ashwini S. Mane, Department of Computer Engineering, Sinhgad College of Engineering, Pune University ,Pune (M.H), India.
2Prof. P.M. Kamde, Department of Computer Engineering, Sinhgad College of Engineering, Pune University ,Pune (M.H), India.

Manuscript received on 21 July 2013 | Revised Manuscript received on 28 July 2013 | Manuscript published on 30 July 2013 | PP: 104-106 | Volume-2 Issue-3, July 2013 | Retrieval Number: C0741072313/2013©BEIESP
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Abstract: In today’s world, information is indispensable in each and every activity. Precise retrieval of information according to the user’s requirement is the dire need of the day. A Content Based Video Retrieval System, in a nutshell, aims at assisting a user to retrieve a video sequence target within a potentially large database. Content-based Video Retrieval Systems (CBVRS) are less common and is even now a research area. There are no existing systems of CBVRS in use because of various restrictions like video size, characteristics, high error rate etc. Search engines like Google etc use textual annotations to retrieve videos for the user which has very high error rate. Content based video retrieval has lots of applications in varied areas like medical sciences, news broadcasting, advertizing, video archiving and will surely revolutionize the field of information technology. A consequence of the growing consumer demand for visual information is sophisticated technology that is needed for representing, modeling, indexing and retrieving multimedia data. In particular, we need robust techniques to index/retrieve and compress visual information. Therefore this system Video Classification using SVM will play an important role in information retrieval and information storage.
Keywords: Direct Frame Difference, FFT , Mean and Standard Deviation ,Support Vector Machine

Scope of the Article: Classification