Indian Journal of Science and Technology

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