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On-line video collection is getting larger nowadays. It becomes difficult for users to go through the whole collection to find the video of their interest. To allow efficient browsing, search and retrieval, one intuitive solution is to cluster video clips according to their genres automatically. Then, users΄ choices can be narrowed down. Besides on-line video repositories, other applications include managing television broadcasting archives, video conferencing records, etc. The goal of video classification is to automatically place each video title in different categories, such as news, sports, etc. The classification process involves extracting the information from the video clips and classifying them into different classes. In this chapter, we first review related work in this field. Then, two novel features based on the camera shooting process is proposed for video genre classification. These new camera based features exploit the fact that a different genre tends to have different camera effects and user perception. Although a lot of work has been proposed with the consideration of cinematic principles, most extracted features are low-level features without much semantic information.We propose a feature that estimates the number of cameras used in a short time interval. Then, we propose another feature by calculating the distribution of the camera distance, which is approximated by the normalized foreground area of each frame. The block-based motion vector field is adopted to reduce the complexity involved in foreground/background modeling. Preliminary experiment results show that the proposed features capture additional genre-related information so that the video genre can be inferred from the proposed features well. [ABSTRACT FROM AUTHOR]

With a rapidly dropping price in hand-held cameras and video editing software, user-generated contents are popular these days, especially on online video sharing websites. To facilitate efficient management of large video collections, it is essential to be able to separate amateur video contents from professional ones automatically. In this work, we propose several features that take into account the camera operation and the nature of amateur video clips to achieve this goal. In the proposed scheme, we estimate the number of different cameras being used in a short time interval, the shakiness of the camera, and the distance between the camera and the subject. Experimental results on a test video data set demonstrate that the camera usage can be inferred from the proposed features and reliable separation of professional and amateur video contents can be achieved. [ABSTRACT FROM AUTHOR]