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Reseach Article

Video Classification using Sine, Cosine, and Walsh Transform with Bayes, Function, Lazy, Rule and Tree Data Mining Classifier

by Sudeep D. Thepade, Madhura M. Kalbhor
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 3
Year of Publication: 2015
Authors: Sudeep D. Thepade, Madhura M. Kalbhor
10.5120/19296-0732

Sudeep D. Thepade, Madhura M. Kalbhor . Video Classification using Sine, Cosine, and Walsh Transform with Bayes, Function, Lazy, Rule and Tree Data Mining Classifier. International Journal of Computer Applications. 110, 3 ( January 2015), 18-23. DOI=10.5120/19296-0732

@article{ 10.5120/19296-0732,
author = { Sudeep D. Thepade, Madhura M. Kalbhor },
title = { Video Classification using Sine, Cosine, and Walsh Transform with Bayes, Function, Lazy, Rule and Tree Data Mining Classifier },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 3 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number3/19296-0732/ },
doi = { 10.5120/19296-0732 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:24.225596+05:30
%A Sudeep D. Thepade
%A Madhura M. Kalbhor
%T Video Classification using Sine, Cosine, and Walsh Transform with Bayes, Function, Lazy, Rule and Tree Data Mining Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 3
%P 18-23
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Video classification has become one of the important research field as hundreds of videos are generated everyday which implies the need to build the classification system. To build faster and easy classification system, the visual content of video is used. Accuracy of classification depends upon the feature extraction which is one of the most important step in video classification. This paper proposes the use of orthogonal transform to generate the feature vector and to investigate effectiveness of different transforms (Cosine, Sine, and Walsh). Experimentation is carried on different sizes of feature vectors which are formed by taking fractional coefficients of row mean of column transformed video frames. Classification algorithm from different families such as Bayes (Naive Bayes and Bayes Net), Function (RBFNetwork and Simple Logistic), Lazy (IB1 and Kstar), Rule (Decision and Part) and Tree (BFTree, J48 Random Tree and Random Forest) are used for classification. Experimental results and its analysis have shown the Simple Logistic classifier with Sine transform to be better for proposed data mining based video classification technique.

References
  1. Dr. H. B. Kekre, Dr. Tanuja K. , Sarode, Jagruti K. Save, "Image Classification in Transform Domain", (IJCSIS) International Journal of Computer Science and Information Security,Vol. 10, No. 3, March 2012
  2. Dr. Sudeep D. Thepade, Madhura M. Kalbhor, "Novel Data Mining based Image Classification with Bayes, Tree, Rule, Lazy and Function Classifiers using Fractional Row Mean of Cosine, Sine and Walsh Column Transformed Images. " 2015 International Conference on Communication, Information & Computing Technology (ICCICT), Jan. 16-17, Mumbai, India
  3. H. B. Kekre, Sudeep D. Thepade, Akshay Maloo "Performance Comparison for Face Recognition using PCA, DCT &WalshTransform of Row Mean and Column Mean", ICGST International Journal on Graphics, Vision and Image Processing (GVIP), Volume 10, Issue II, pp. 9-18, June 2010.
  4. http://infolab. stanford. edu/~wangz/project/imscreen/IDMS98/wang. ps. Z
  5. Dr. Sudeep D. Thepade, Rik Das," Performance Comparison of Feature Vector Extraction Techniques in RGB Color Space using Block Truncation Coding for Content Based Image Classification with Discrete classifiers" INDICON 2014.
  6. Sudeep Thepade, Rik Das, and Saurav Ghosh, A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification, Journal of Engineering Volume 2014 (2014).
  7. J. Han and M. Kamber, (2000) "Data Mining: Concepts and Techniques," Morgan Kaufmann.
  8. Mizianty, M. ; Kurgan, L. ; Ogiela, M. ," Comparative Analysis of the Impact of Discretization on the Classification with Naïve Bayes and Semi-Naïve Bayes Classifiers". Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference.
  9. Ian H. Witten and Elbe Frank, (2005) "Datamining Practical Machine Learning Tools and Techniques," Second Edition, Morgan Kaufmann, San Fransisco.
Index Terms

Computer Science
Information Sciences

Keywords

Content based video classification Transform Cosine Sine Walsh Classifier Bayes Function Lazy Rule Tree classifier Fractional content.