DocumentCode :
2770007
Title :
An efficient least common subgraph algorithm for video indexing
Author :
Shearer, Kim ; Venkatesh, Svetha ; Bunke, Horst
Author_Institution :
Sch. of Comput. Sci., Curtin Univ. of Technol., Perth, WA, Australia
Volume :
2
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
1241
Abstract :
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 priori 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
Keywords :
computational complexity; content-based retrieval; database indexing; graph theory; video databases; graph matching algorithms; largest common subgraph; least common subgraph algorithm; online classification; similarity; video indexing; Application software; Area measurement; Computer science; Computer vision; Image databases; Image retrieval; Indexing; Information retrieval; Query processing; Time measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711924
Filename :
711924
Link To Document :
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