Title :
Inexact graph retrieval
Author :
Huet, Benoit ; Hancock, Edwin R.
Author_Institution :
Dept. of Comput. Sci., York Univ., UK
Abstract :
The paper describes a graph matching technique for recognising line pattern shapes in large image databases. We use a Bayesian matching algorithm that draws on edge consistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node feature vectors are constructed by computing normalised histograms of pairwise geometric attributes. Attribute similarity is assessed by computing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the database which has the largest a posteriori probability
Keywords :
Bayes methods; content-based retrieval; graph theory; image matching; very large databases; visual databases; Bayesian matching algorithm; Bhattacharyya distance; a posteriori probability; attribute similarity; edge consistency; graph matching technique; inexact graph retrieval; large image databases; line pattern shape recognition; node attribute similarity; node feature vectors; normalised histograms; pairwise geometric attributes; query graph; Bayesian methods; Computer science; Content based retrieval; Histograms; Image recognition; Image retrieval; Information retrieval; Layout; Object recognition; Shape;
Conference_Titel :
Content-Based Access of Image and Video Libraries, 1999. (CBAIVL '99) Proceedings. IEEE Workshop on
Conference_Location :
Fort Collins, CO
Print_ISBN :
0-7695-0034-X
DOI :
10.1109/IVL.1999.781121
