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