Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Performance Comparison of Various Decision Tree Algorithms for Classification of Advertisement and Non Advertisement Videos


Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore − 641 046, Tamil Nadu, India
2 CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia, Saudi Arabia
3 Department of Computer Science and Engineering, Faculty of Engineering and Technology, S.R.M University, Kattankulathur − 603203, Tamil Nadu, India
4 VIT University, Chennai Campus, Vandalur − 600127, Kelambakkam Road, Chennai, India
 

Background/Objectives: The main objective of the present study is to do the prerequisite process to develop a viewerfriendly electronic embedded system and business beneficial system to promote their products. This can be achieved by classifying the extracted Advertisement (ADD) videos from the Non-Advertisement (NADD) videos which consists of more visual information. Methods/ Statistical Analysis: The proposed frame work facilitates to identify the advertisement and non advertisement videos from the live stream television videos are discussed. The Block Intensity Comparison Code (BICC) technique is applied to extract the essential features from the ADD and NADD video frames. The frames are divided into various block sizes to select the best performing block size of the frame. The 8x8 frame size has been chosen as the promising block size to conduct the experiments. An extensive experimental analysis has been demonstrated with different classifier and a comparative study also reported. Findings: Decision tree algorithm (C4.5) has been employed to identify the vibrant features and these features are taken as the input to the various decision tree algorithms, namely J48, J48graft, LM tree, Random tree, BF tree, REP tree and NB tree to classify the video genre. A broad investigation has been made by a random tree algorithm which produced better predictive performance than the other algorithms. The training and the optimization of random tree model with their essential parametric measures are reported. Based on the overall study, random tree with BICC feature was found as the most preferred classification algorithm that achieved the 92.08% than the other algorithms. The classification capability and the performance evaluation of random tree algorithm with block intensity comparison code is reported and discussed for further study. Application/Improvements: The performance of the classifier can also be improved with other novel features.

Keywords

Advertisement (ADD) Videos, Block Intensity Comparison Code (BICC) Features, Classification, Non- Advertisement (NADD) Videos.
User

Abstract Views: 152

PDF Views: 0




  • Performance Comparison of Various Decision Tree Algorithms for Classification of Advertisement and Non Advertisement Videos

Abstract Views: 152  |  PDF Views: 0

Authors

B. Rebecca Jeya Vadhanam
Research and Development Centre, Bharathiar University, Coimbatore − 641 046, Tamil Nadu, India
S. Mohan
CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia, Saudi Arabia
V.V. Ramalingam
Department of Computer Science and Engineering, Faculty of Engineering and Technology, S.R.M University, Kattankulathur − 603203, Tamil Nadu, India
V. Sugumaran
VIT University, Chennai Campus, Vandalur − 600127, Kelambakkam Road, Chennai, India

Abstract


Background/Objectives: The main objective of the present study is to do the prerequisite process to develop a viewerfriendly electronic embedded system and business beneficial system to promote their products. This can be achieved by classifying the extracted Advertisement (ADD) videos from the Non-Advertisement (NADD) videos which consists of more visual information. Methods/ Statistical Analysis: The proposed frame work facilitates to identify the advertisement and non advertisement videos from the live stream television videos are discussed. The Block Intensity Comparison Code (BICC) technique is applied to extract the essential features from the ADD and NADD video frames. The frames are divided into various block sizes to select the best performing block size of the frame. The 8x8 frame size has been chosen as the promising block size to conduct the experiments. An extensive experimental analysis has been demonstrated with different classifier and a comparative study also reported. Findings: Decision tree algorithm (C4.5) has been employed to identify the vibrant features and these features are taken as the input to the various decision tree algorithms, namely J48, J48graft, LM tree, Random tree, BF tree, REP tree and NB tree to classify the video genre. A broad investigation has been made by a random tree algorithm which produced better predictive performance than the other algorithms. The training and the optimization of random tree model with their essential parametric measures are reported. Based on the overall study, random tree with BICC feature was found as the most preferred classification algorithm that achieved the 92.08% than the other algorithms. The classification capability and the performance evaluation of random tree algorithm with block intensity comparison code is reported and discussed for further study. Application/Improvements: The performance of the classifier can also be improved with other novel features.

Keywords


Advertisement (ADD) Videos, Block Intensity Comparison Code (BICC) Features, Classification, Non- Advertisement (NADD) Videos.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i48%2F140349