International Journal of Information Technology & Computer Science ( IJITCS )
Video genre classification, video contents retrieval and semantics research are very attractive for many researchers in video processing and analysis domain. Many researchers try to propose structure or frameworks to classify the video genre that’s integrating many algorithms using low and high level features. Features generally include both useful and useless information that are difficult to separate. In this paper, video genre classification is proposed by using only the audio channel. A decomposition model is based on multivariate adaptive regression splines to separate useful and useless components and the genre identification is performed on these low-level acoustic features such as MFCC and timbral textual features. Factor Analysis is proposed to reduce feature dimension in this system. For comparison, it is implemented the three feature dimension methods such as Principal Component Analysis, Stochastic Proximity Embedding algorithm, Stochastic Neighbor Embedding . MARS method is used as classifier, to classify the genre type,. Experiments are conducted on a corpus composed from Cartoon, Sport, News, Dahma and Music .
: Principal Component Analysis, Stochastic Proximity Embedding algorithm, Stochastic Neighbor Embedding, Factor Analysis
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