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Classification of Non-edited Broadcast Video Using Holistic Low-level Features / Roach Matthew; Xu Li-qun; Mason John; Heath Martlesham; Ip Ipswich

Int. Tyrrhenian Workshop on Digital Communications (IWDC)

Swansea University Author: Roach, Matthew

Abstract

A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one h...

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Published in: Int. Tyrrhenian Workshop on Digital Communications (IWDC)
Published: 2002
URI: https://cronfa.swan.ac.uk/Record/cronfa39129
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Abstract: A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one hour long in total for each genre with an even split of training and testing data. This paper reports the classification result for the two media modes - acoustic and visual - used separately, and for a linear fusion of the two modes at the score level. The correaIonding classification accuracy achieved is approximately 74%, 73%, and 87%, reaIectively, given a 30-second decision window, or for every 30 second non-overlap video segment an answer of its identity is provided Further analysis of the confusion matrices of the classification errors is given. The extension of the approach to the open-set video genre verification and semantic scene segmentation and classification is expected.
College: College of Science