Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/7806
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dc.contributor.authorKrishna, Mohan, C.
dc.contributor.authorYegnanarayana, B.
dc.date.accessioned2020-03-30T10:02:50Z-
dc.date.available2020-03-30T10:02:50Z-
dc.date.issued2007
dc.identifier.citationProceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007, 2007, Vol., , pp.559-564en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7806-
dc.description.abstractIn this paper, we propose a method for sports videos genre classification using edge-based feature, namely edge direction histogram and edge intensity histogram. We demonstrate that these features provide discriminative information useful for sports video classification. We use hidden Markov models (HMMs) to model the sports video categories. Evidence from the two edge based features are combined using a linear weighting rule. We also show that combining evidence from complementary edge features results in improved classification performance. We demonstrate the application of this framework to five sport genre types, namely, cricket, football, tennis, basketball and volleyball. Copyright � 2007 IICAI.en_US
dc.titleEdge-based sports video classification using HMMen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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