Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results. 1. Introduction Recently, video usage gains increasing importance, especially with the advance of recent internet technology and digital media; people have access to huge amount of data through internet and television. It is difficult for people to find videos of interest among these tremendous amounts of data and use them when there is a need, and it is not feasible to watch all the videos searching for the one of interest. Exploiting content-based of video footage is continuously needed for many applications, for example, for retrieving video sequence, creating automatic video summarization, or detecting specific action or activities in a video surveillance [1]. Many works are done dealing with video classification problem by categorizing videos in certain categories or genre, bridging the wide semantic gap between computed low-level features and high-level concepts and helping people to find their videos of interest within narrow domain. To get good understanding of video content, many different techniques have been developed and different video features have been identified for better video representation. Many techniques are used for video classification such as Bayesian, Hidden Markov Model (HMM), Gaussian Mixture Model (GMM), Neural-Network (NN), and Support Vector Machine (SVM). SVM is considered a state-of-the-art algorithm for classifying binary data through its implementation of kernels. Oh et al. developed general framework to perform the fundamental tasks for video data mining which are temporal segmentation of video sequences and feature extraction and how to capture the location of motions occurring in a segment, how to cluster those segmented pieces, and how to find whether a segment has normal or abnormal events [2]. Chen et al. proposed a multimedia data mining framework for extraction of soccer goal events in soccer videos, by using combined multimodal
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