Abstract
Video content is evolving enormously with the heavy usage of internet and social media websites. Proper searching and indexing of such video content is a major challenge. The existing video search potentially relies on the information provided by the user, such as video caption, description and subsequent comments on the video. In such case, if users provide insufficient or incorrect information about the video genre, the video may not be indexed correctly and ignored during search and retrieval. This paper proposes a mechanism to understand the contents of video and categorize it as Music Video, Talk Show, Movie/Drama, Animation and Sports. For video classification, the proposed system uses audio and visual features like audio signal energy, zero crossing rate, spectral flux from audio and shot boundary, scene count and actor motion from video. The system is tested on popular Hollywood, Bollywood and YouTube videos to give an accuracy of 96%.
Highlights
IntroductionThe term content-based automatic video genre identification means, to recognize the category of a video on basis of its contents
The word genre is defined as socially agreed category of content
A reliable automatic video genre identification system which can categorize any type of video, is yet to be proposed
Summary
The term content-based automatic video genre identification means, to recognize the category of a video on basis of its contents. The heterogeneous nature of video contents, makes the genre identification a challenging job. The nature of video contents is diverse as it combines all other types of media such as text, audio and image [4]. The top ranking social networking sites like Facebook, YouTube allow users to explore billions of videos per day. Proper organization of such videos is a necessary operation to ensure efficient indexing and searching. A reliable automatic video genre identification system which can categorize any type of video, is yet to be proposed
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