B. Rebecca Jeya Vadhanam ¹, S. Mohan ², V. Sugumaran ³

Affiliations
1 Research and Development Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia
3 SMBS, VIT University, Chennai Campus, Chennai – 600127, Tamil Nadu, IN

Abstract

Objectives: In this present study, there are various methods and techniques that are reviewed to dig the hidden information from the video frames to process the live stream Television (TV) videos. Video classification is an emerging trend that is intended to classify the Advertisement (ADD) videos from the television programme. Classification of ADD videos from the general programs provides an efficient approach to manage and utilize the ADD video data. Detection of ADD video plays a major role for advertisement content management, advertisement for targeted customers, querying, retrieving, inserting, and skipping the advertisement to view the desired channels. Detection of advertisement frames creates a unique application in the multimedia systems. Methods/Analysis: The process of feature extraction which enables recognition of ADD videos and Non Advertisement (NADD) videos directly from the TV streams are discussed. The features are extracted using Block Intensity Comparison Code (BICC) technique. BICC technique is applied on various block sizes of a frame and the best performing block size 8×8 has been chosen for the experimental study. Decision tree (J48) algorithm and BICC feature are utilized to find out the promising block size of the frame. The best features are identified and selected by decision tree (J48) algorithm. Artificial Immune Recognition System (AIRS) is applied on these features to classify the ADD class and NADD class. The AIRS classification algorithms are motivated by the biological immune system components that include important and unique abilities. These algorithms recreate the specialities of the immune framework like; discrimination, learning, and the memorizing methodology in place are utilized to classification and pattern recognition. AIRS2 algorithm is parallelism, separating the dataset into number segments and handling them exclusively. Findings: In this study, three versions of AIRS algorithms, namely, AIRS1, AIRS2 and AIRS2 parallel are used for classification with BICC feature. AIRS2 parallel classifier performed better compared with AIRS1 and AIRS2. The present study proved the biological immune recognition based AIRS algorithm out performs than various classifiers in terms of reliability and classification accuracy. The classification capability and the efficiency of AIRS2 parallel algorithm with BICC feature has been compared among various classifiers and reported. Application/Improvements: This study is very much helpful and essential for television viewers and the busy current generation to skip the nuisance of advertisements to enjoy watching their favourable shows of various television channels. The proposed work is useful for demands on video and video content management systems. This work can also be extended with novel feature set to improve the classifiers performance for efficient video classification and retrieval systems.

Keywords

Television Live Stream (TV), Advertisement Frames (ADD), Non Advertisement Frames (NADD), Block Intensity Comparison Code (BICC), Decision Tree, Artificial Immune Recognition System (AIRS) Classification

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V. V. Ramalingam ¹, S. Mohan ², B. Rebecca Jeya Vadhanam ³, V. Sugumaran ⁴

Affiliations
1 Department of Computer Science and Engineering, S. R. M University, Kattankulathur – 603203, Tamil Nadu, IN
2 CCIS, AL Yamamah University, Riyadh, Kingdom of Saudi Arabia
3 Department of Computer Applications, Faculty of Science and Humanities, S. R. M University, Kattankulathur – 603203, Tamil Nadu, IN
4 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai-600127, Tamil Nadu,, IN

Abstract

Objectives: This paper presented a novel approach with a set of Auto Regressive Moving Average (ARMA) features for the best classification of different hand moments in Electroencephalogram (EEG) signals using C4.5 Decision tree algorithm. Methods/Analysis: The characteristics of EEG signals can be represented through the best features is the most prominent and significant role in the classification systems. The classification is more flawless when the specimen is streamlined through the feature extraction and feature selection process. Findings: In this study, there are four kinds of EEG signals recorded from strong volunteers with finger open, finger close, wrist clockwise and wrist counterclockwise. The well performing statistical features are acquired from the EEG signals. C4.5 Decision tree classifier is used to identify the changes in the EEG signals. The yield of the classifier confirmed that the proposed C4.5 Decision tree classifier has potential to classify the EEG signals of the specific hand movements. Improvement: The proposed work is contributed to manage the right hand movements through the EEG signals. The efficient techniques are required to process the complex EEG signals to achieve the better classification result. To improve the classification accuracy, an efficient feature extraction technique may be applied.

Keywords

ARMA Features, C4.5 Decision Tree, Classification, Electroencephalogram (EEG) Signals.

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B. Rebecca Jeya Vadhanam ¹, S. Mohan ², V.V. Ramalingam ³, V. Sugumaran ⁴

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

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.

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