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Abstract: This paper uses a neural network and a k-nearest neighbor classifier for modelling transitions in video based on a number of statistical measures derived from video images. The transitions modelled include: cut, fade in, fade out, dissolve, tilt up, tilt down, pan left and pan right. The aim of this study is to develop a machine learning system that learns on the basis of training data to predict these transitions from one frame to another in a test video sequence. The results show that... (Update)
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BibTeX entry: (Update)
@misc{ ren-video,
author = "Wei Ren and Sameer Singh",
title = "Video Transition: Modelling and Prediction",
url = "citeseer.ist.psu.edu/716678.html" }
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