Abstract
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.
Highlights
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
Weighted Kernel Logistic Regression (WKLR) is implemented on the prepared data for classification aiming to achieve significant accuracy and making WKLR to be an effective method for video classification
The data distributed randomly, the algorithm of WKLR MLE was implemented with 10-fold crossvalidation, and max number of iterations is set to 30 for truncated Newton method
Summary
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. 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)
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