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Extraction and Classification of Self-consumable Sport Video Highlights Using Generic HMM

By Dian W. Tjondronegoro, Yi-Ping Phoebe Chen and Binh L. Pham

Abstract

This paper aims to automatically extract and classify self-consumable sport video highlights. For this purpose, we will emphasize the benefits of using play-break sequences as the effective inputs for HMM-based classifier. HMM is used to model the stochastic pattern of high-level states during specific sport highlights which correspond to the sequence of generic audio-visual measurements extracted from raw video data. This paper uses soccer as the domain study, focusing on the extraction and classification of goal, shot and foul highlights. The experiment work which uses183 play-break sequences from 6 soccer matches will be presented to demonstrate the performance of our proposed scheme

Topics: 080106 Image Processing, 080305 Multimedia Programming, 080199 Artificial Intelligence and Image Processing not elsewhere classified, Self, consumable highlights, sport video summarization, Hidden Markov Model (HMM), audio, visual features
Year: 2005
OAI identifier: oai:eprints.qut.edu.au:4940

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