An efficient least common subgraph algorithm for video indexing
Contribuinte(s) |
Jain, Anil K. Venkatesh, Svetha Lovell, Brian Carrington |
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Data(s) |
01/01/1998
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Resumo |
Many tasks in computer vision can be expressed as graph problems. This allows the task to be solved using a well studied algorithm, however many of these algorithms are of exponential complexity. This is a disadvantage when considered in the context of searching a database of images or videos for similarity. Work by Mesaner and Bunke (1995) has suggested a new class of graph matching algorithms which uses a <i>priori</i> knowledge about a database of models to reduce the time taken during online classification. This paper presents a new algorithm which extends the earlier work to detection of the largest common subgraph.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30044870/venkatesh-anefficient-1998.pdf http://dx.doi.org/10.1109/ICPR.1998.711924 |
Direitos |
1998, IEEE |
Palavras-Chave | #graph matching algorithms #largest common subgraph #least common subgraph algorithm #online classification #similarity #video indexing |
Tipo |
Conference Paper |