Inexact graph matching using a genetic algorithm for image recognition

Auwatanamongkol, Surapong

doi:10.1016/j.patrec.2007.02.013

Cited by 29 publications

(7 citation statements)

References 14 publications

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“…Since the introduction of these first theoretical indicators, the GM problems gradually gained in importance and various new closeness measures were proposed, especially for pattern and image recognition [1,5,9,37] or chemistry informatics [23]. They usually present ad-hoc measures, using application-specific features that might be difficult to express in a unified GI context.…”

Section: Inexact Algorithms For Graph Isomorphismmentioning

confidence: 99%

“…More specific algorithms (i.e. using decision trees, neural networks) are also available in the literature [1,20,28,38] but they are less related to our paper.…”

Section: Inexact Algorithms For Graph Isomorphismmentioning

confidence: 99%

“…using angles [1] or using various specific feature-based graph descriptions [39]), but the goal of the paper is not to enter into complex formulas. However, a common point of all these objective functions is that they require algorithms with high computational costs that are usually applied on small graphs (|V | < 1000) as GM problems are computationally very demanding.…”

Section: Objective Functionsmentioning

confidence: 99%

“…The graph isomorphism (GI) problem and its generalizations are essential in large application areas directly dealing with similarity problems, e.g. pattern and image recognition [1,8,9,20,28,33,35,38,39] or chemical informatics [23,31]. Furthermore, algorithms for these problems often find unexpected applications in other more specific applications, from biometric identification or scheduling to monitoring computer networks-numerous such examples are available in the literature [8,10].…”

Section: Introductionmentioning

confidence: 99%

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Isomorphism Testing via Polynomial-Time Graph Extensions

Porumbel

2010

J Math Model Algor

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This paper deals with algorithms for detecting graph isomorphism (GI) properties. The GI literature consists of numerous research directions, from highly theoretical studies (e.g. defining the GI complexity class) to very practical applications (pattern recognition, image processing). We first present the context of our work and provide a brief overview of various algorithms developed in such disparate contexts. Compared to well-known NP-complete problems, GI is only rarely tackled with general-purpose combinatorial optimization techniques; however, classical search algorithms are commonly applied to graph matching (GM). We show that, by specifically focusing on exploiting isomorphism properties, classical GM heuristics can become very useful for GI. We introduce a polynomial graph extension procedure that provides a graph coloring (labeling) capable of rapidly guiding a simple-but-effective heuristic toward the solution. The resulting algorithm (GIExt) is quite simple, very fast and practical: it solves GI within a time in the region of O(|V | 3 ) for numerous graph classes, including difficult (dense and regular) graphs with up to 20.000 vertices and 200.000.000 edges. GI-Ext can compete with recent state-of-the-art GI algorithms based on well-established GI techniques (e.g. canonical labeling) refined over the last three decades. In addition, GI-Ext also solves certain GM problems, e.g. it detects important isomorphic structures induced in non-isomorphic graphs.

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Section: Inexact Algorithms For Graph Isomorphismmentioning

confidence: 99%

“…More specific algorithms (i.e. using decision trees, neural networks) are also available in the literature [1,20,28,38] but they are less related to our paper.…”

Section: Inexact Algorithms For Graph Isomorphismmentioning

confidence: 99%

Section: Objective Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Isomorphism Testing via Polynomial-Time Graph Extensions

Porumbel

2010

J Math Model Algor

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“…probabilistic relaxation (Bengoetxea et al, 2002;Coughlan and Ferreira, 2002;Christmas and Kittler, 1995), EM algorithm (Cross and Hancock, 1998;Luo and Hancock, 2000), neural networks (Lee and Park, 2002;Lee and Liu, 2000), decision trees (Messmer and Bunke, 1999) and a genetic algorithm (Cross and Hancock, 1996;Auwatanamongkol, 2007). Let us now give an overview of the main approaches and report on some of the most representative references.…”

Section: Introductionmentioning

confidence: 99%

A graph matching method and a graph matching distance based on subgraph assignments

Raveaux

Burie

Ogier

2010

Pattern Recognition Letters

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A One Hour Trip in the World of Graphs, Looking at the Papers of the Last Ten Years

Vento

2013

Graph-Based Representations in Pattern Recognition

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The use of a graph-based pattern representation induces the need to formulate the main operations required in Pattern Recognition in terms of operations on graphs: classification, usually intended as the comparison between an object and a set of prototypes, and learning, which is the process for obtaining a model of a class starting from a set of known samples, are among the key issues that must be addressed using graph-based techniques.\ud \ud Forty years have passed since the first papers on this topic appear in Pattern Recognition literature: a lot of research effort has been devoted to explore this challenging field and some approaches have been meanwhile consolidated. These notes aren’t a scientific paper but some considerations inspiring my future talk at gbr 2013 conference, a little trip in the word of graphs aimed at better knowing treasures and outstanding locations

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Inexact graph matching using a genetic algorithm for image recognition

Cited by 29 publications

References 14 publications

Isomorphism Testing via Polynomial-Time Graph Extensions

Isomorphism Testing via Polynomial-Time Graph Extensions

A graph matching method and a graph matching distance based on subgraph assignments

A One Hour Trip in the World of Graphs, Looking at the Papers of the Last Ten Years

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