Inexact Graph Retrieval

Authors:
Benoit Huet;Edwin R. Hancock
Affiliations:
-;-
Venue:
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Year:
1999

Citing 10
Cited 1

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A Graduated Assignment Algorithm for Graph Matching

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Structural Matching by Discrete Relaxation

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COSMOS-A Representation Scheme for 3D Free-Form Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching for shape retrieval

Proceedings of the 1998 conference on Advances in neural information processing systems II
Fuzzy Relational Distance for Large-Scale Object Recognition

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Scene analysis using appearance-based models and relational indexing

ISCV '95 Proceedings of the International Symposium on Computer Vision
Relational Histograms for Shape Indexing

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision

A heuristic for the retrieval of objects in video in the framework of the rough indexing paradigm

Image Communication

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Abstract

This 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 data-base. 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 realized by selecting the candidate from the database which has the largest a posteriori probability.