Abstract
TV Talk Shows are used for exchanging views among participants on a particular topic. Huge audiences, among all age groups, follow the talk shows worldwide to acquire knowledge on current affairs and other topics of their interest. A major portion of these audiences use online video databases to search talk shows of their interest. Online video databases contain videos of different genres like movie, drama, talk shows, animations, sports, horror, music etc. Searching a particular talk show, in presence of many other video files, is a tedious task, especially when the uploader has not used proper naming convention while assigning caption to the file as search in the video databases is still text based. Different contents based classification techniques have been proposed in literature to efficiently index video contents on Internet. Literature also includes few attempts at identifying if a video clip is representing a talk show; however, this identification already found in literature uses a long list of features which makes its processing slow. This paper proposes a solution based on audio visual features and employing basic grammar of talk shows recording to automatically identify if a video recently uploaded on video database is containing a talk show. We have performed experiments on different genres of videos collected from YouTube, Dialymotion, movies from Bollywood and Hollywood. The proposed system classifies a video file in TalkShow and OtherVideo classes with precision and recall of 93% and 100% respectively.
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Muhammad, A., Daudpota, S.M. (2016). Content Based Identification of Talk Show Videos Using Audio Visual Features. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_20
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DOI: https://doi.org/10.1007/978-3-319-41920-6_20
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