Automatic Genre Classification of TV Programs Using Audio and Face Processing
Pathasu Doungmala, Anupap Meesomboon · Advanced materials research · 2014
In this paper we present a new system to classify TV programs into predefined categories based on the analysis of their audio and video contents. This is very useful in intelligent display and storage systems that can select channels and record or skip contents according to the consumer's preference. Distinguishable patterns exist in different categories of TV programs in terms of human faces and audio. In this paper four categories divided into news, cartoon, variety and sport are of interest. News and variety have differences between frames less than sport and cartoon. For audio feature, we apply short time energy, zero crossing, spectral centroid and short time Fourier transform for feature extraction. For face feature, in the first step, Haar like feature is employed for face detection and eigenface is then applied for feature extraction. Then, neural network is implemented for classification. From experimental results, classification rate of 95% accuracy which is better than the other paper is achievable.