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Proceedings Paper

Intelligent video editing system using a neural network coding scheme
Author(s): Richard M. Rickman; T. John Stonham
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Paper Abstract

Video editors are frequently required to access sections of a video sequence which contain a particular scene. This may be regarded as an image retrieval-by-content problem where the user wishes to select images from within a large database according to a measure of similarity to a target. We present an intelligent video editing system based on a neural network coding scheme. The transformation learnt by the neural network maps each image into a very compact index which supports rapid fuzzy matching of video images. The neural network is trained using a learning law which produces an information preserving transform. Trained in this way, the node learns features which characterize the distribution of scenes within the video sequence. Each image frame in the sequence is coded with respect to these features. We show how the system performs on a typical sequence of newsreel footage and discuss the factors affecting the performance of both the training and the retrieval mechanism.

Paper Details

Date Published: 23 March 1995
PDF: 7 pages
Proc. SPIE 2420, Storage and Retrieval for Image and Video Databases III, (23 March 1995); doi: 10.1117/12.205302
Show Author Affiliations
Richard M. Rickman, Brunel Univ. (United Kingdom)
T. John Stonham, Brunel Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 2420:
Storage and Retrieval for Image and Video Databases III
Wayne Niblack; Ramesh C. Jain, Editor(s)

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