International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 115 | Views: 165

M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 3 Issue 10, October 2014

Video Sequence Matching Based On Multiclass SVM Classifiers and RGB Component Relations

Meera R. Nath

Abstract: VIDEO SEQUENCE MATCHING BASED ON MULTICLASS SVM CLASSIFIERS AND RGB COMPONENT RELATIONS. The concept of RGB feature extraction and multiclass SVM Classifiers can be used to locate an input video in a database of videos. The proposed system aims at providing matching video for the query video if present in the database. The proposed system helps in the area of copyright protection of videos and also for reducing the storage redundancy. The system uses the concept of both the SVM (Support Vector Machine) classifiers and RGB components. Firstly, each key frame of the video is split into non overlapping blocks. The order of average intensity of the red, green and blue components of each pixel of the non overlapping block and then of the key frame is determined. There are six cases which show the relation of red, green and blue components which in turn are used to create the feature set of the color frame and then video. The feature of the database videos are saved and query video is found using RGB intensity variations. The video sequence matching operation is performed using the multiclass SVM classifiers. Multiclass SVM can be used to classify data into multiple classes. In Multiclass SVM, two functions are present. First one is svmtrain () which trains the Support Vector Machine using the training data. Second, svmclassify () which is used to classify new data using this trained Support Vector Machine. Here svmtrain () trains the feature set of the database videos and create model for each of them. svmclassify () classifies the query video to any of the database videos class using the trained model. Thus with the help of these two functions the query video could be located in the video database if present. The proposed system provides more accuracy and fastness in determining the matching video. The computational complexity and storage complexity is very low and space complexity is satisfactory for the proposed system.

Keywords: Content Based Technology, Suppoert Vector Machine, Colour Histogram

Edition: Volume 3 Issue 10, October 2014,

Pages: 1938 - 1944

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