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Median graphs: A genetic approach based on new theoretical properties

pattern recognition 2009年第9期42卷 2003-2012页

作者： Ferrer, M. Valveny, E. Serratosa, F. Univ Autonoma Barcelona Dept Ciencies Comp Ctr Visio Comp Bellaterra 08193 Spain Univ Rovira & Virgili Dept Engn Informat & Matemat Tarragona 43007 Spain

Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Median graph Genetic search Maximum common subgraph Graph matching structural pattern recognition

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Using attributed plex grammars for the generation of image and graph databases

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pattern recognition LETTERS 2003年第8期24卷 1081-1087页

作者： Hagenbuchner, M Gori, M Bunke, H Tsoi, AC Irniger, C Univ Wollongong Fac Informat Wollongong NSW 2500 Australia Univ Siena Dip Ingn Informazione I-53100 Siena Italy Univ Bern Inst Comp Sci & Math CH-3012 Bern Switzerland

In this paper, a methodology for the generation of benchmarks in pattern recognition is described. The patterns are represented by means of an attributed plex language, which are based on plex grammars augmented by attributes. It is shown that the generated patterns are particularly suitable for the extraction of graph-based representations. As a result, databases of artificial pictures and correspondent graphs can be generated. These collections of graphs are very appropriate for benchmarks in the area of structural pattern recognition, since they are originated from a grammar and not from random distributions. The tools for creating the databases are public domain and have been already used for benchmarking artificial neural networks operating on structured domains. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： benchmarking attribute grammars graph-based representations graphical databases structural pattern recognition plex grammars

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First order Gaussian graphs for efficient structure classification

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pattern recognition 2003年第6期36卷 1311-1324页

作者： Bagdanov, AD Worring, M Univ Amsterdam Fac Sci NL-1098 SJ Amsterdam Netherlands

First order random graphs as introduced by Wong are a promising too] for structure-based classification. Their complexity, however, hampers their practical application. We describe an extension to first order random graphs which uses continuous Gaussian distributions to model the densities of all random elements in a random graph. These First Order Gaussian Graphs (FOGGs) are shown to have several nice properties which allow for fast and efficient clustering and classification. Specifically, we show how the entropy of a FOGG may be computed directly from the Gaussian parameters of its random elements. This allows for fast and memoryless computation of the objective function used in the clustering procedure used for learning a graphical model of a class. We give a comparative evaluation between FOGGs and several traditional statistical classifiers. On our example problem, selected from the area of document analysis, our first order Gaussian graph classifier significantly outperforms statistical, feature-based classifiers. The FOGG classifier achieves a classification accuracy of approximately 98%, while the best statistical classifiers only manage approximately 91%. (C) 2002 pattern recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： structural pattern recognition first order random graphs document analysis genre classification

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Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2013年第7期35卷 1539-1551页

作者： Vogelstein, Joshua T. Roncal, William Gray Vogelstein, R. Jacob Priebe, Carey E. Duke Univ Dept Math & Stat Durham NC 27708 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 20723 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA

This manuscript considers the following "graph classification"question: Given a collection of graphs and associated classes, how can one predict the class of a newly observed graph? To address this question, we propose a statistical model for graph/class pairs. This model naturally leads to a set of estimators to identify the class-conditional signal, or "signal-subgraph,"defined as the collection of edges that are probabilistically different between the classes. The estimators admit classifiers which are asymptotically optimal and efficient, but which differ by their assumption about the "coherency"of the signal-subgraph (coherency is the extent to which the signal-edges "stick together"around a common subset of vertices). Via simulation, the best estimator is shown to be not just a function of the coherency of the model, but also the number of training samples. These estimators are employed to address a contemporary neuroscience question: Can we classify "connectomes"(brain-graphs) according to sex? The answer is yes, and significantly better than all benchmark algorithms considered. Synthetic data analysis demonstrates that even when the model is correct, given the relatively small number of training samples, the estimated signal-subgraph should be taken with a grain of salt. We conclude by discussing several possible extensions.

关键词： Statistical inference graph theory network theory structural pattern recognition connectome classification

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Extraction of building polygons from SAR images: Grouping and decision-level in the GESTALT system

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pattern recognition LETTERS 2010年第10期31卷 1071-1076页

作者： Michaelsen, Eckart Stilla, Uwe Soergel, Uwe Doktorski, Leo Fraunhofer FOM Forschungsinst Optron & Mustererkennung D-76275 Ettlingen Germany

The GESTALT system is a stratified architecture designed for challenging computer vision tasks. As example application building recognition from high-resolution SAR-data of three urban scenes of different types is presented. This contribution focuses on the 3rd and 4th layers of the system: the grouping and decision layers. The 3rd layer contains an assessment driven perceptual grouping process with any-time capability and flexible control. Object grouping is based on the principles of Gestalt psychology such as similarity, continuity, and symmetry. A dynamic programming optimization is used in the final decision layer to assemble closed polygons delineating building outlines. Further post-processing includes polygon editing and consistency enforcement. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Building recognition SAR Gestalt perception Dynamic programming structural pattern recognition

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On the Impact of Using Utilities Rather than Costs for Graph Matching

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NEURAL PROCESSING LETTERS 2018年第2期48卷 691-707页

作者： Riesen, Kaspar Fischer, Andreas Bunke, Horst Univ Appl Sci FHNW Inst Informat Syst Riggenbachstr 16 CH-4600 Olten Switzerland Univ Fribourg Dept Informat CH-1700 Fribourg Switzerland HES SO CH-1700 Fribourg Switzerland Univ Bern Inst Comp Sci Neubruckstr 10 CH-3012 Bern Switzerland

The concept of graph edit distance constitutes one of the most flexible graph matching paradigms available. The major drawback of graph edit distance, *** exponential time complexity, has been recently overcome by means of a reformulation of the edit distance problem to a linear sum assignment problem. However, the substantial speed up of the matching is also accompanied by an approximation error on the distances. Major contribution of this paper is the introduction of a transformation process in order to convert the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process. We empirically confirm the positive effects of this transformation on five benchmark graph sets with respect to the accuracy and run time of a distance based classifier.

关键词： structural pattern recognition Graph matching Greedy graph edit distance Utility matrix

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Error-tolerant geometric graph similarity and matching

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pattern recognition LETTERS 2019年 125卷 625-631页

作者： Dwivedi, Shri Prakash Singh, Ravi Shankar Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

Geometric graph matching is the process of evaluating the similarity between geometric graphs, where each vertex has an associated coordinate point in a plane. Exact matching computes a strict correspondence between two geometric graphs, whereas in error-tolerant matching an approximate similarity between two geometric graphs is computed. In this paper, we present an approach to error-tolerant graph matching using geometric graphs. We introduce the vertex distance (dissimilarity) and edge distance between two geometric graphs and use it to compute graph distance. Finally, we use graph distance to perform error-tolerant geometric graph matching. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Graph matching Geometric graph structural pattern recognition

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Graph edit distance as a quadratic assignment problem

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pattern recognition LETTERS 2017年 87卷 38-46页

作者： Bougleux, Sebastien Brun, Luc Carletti, Vincenzo Foggia, Pasquale Gauzere, Benoit Vento, Mario Normandie Univ UNICAEN ENSICAEN CNRSGREYC UMR 6072 6 Bd Marechal Juin F-14050 Caen France Univ Salerno Dept Informat Elect Engn & Appl Math MIVIA Via Giovanni Paolo 2 I-84084 Fisciano SA Italy Normandie Univ INSA Rouen LITIS EA 4108 Ave Univ F-76801 St Etienne Du Rouvray France

The Graph Edit Distance (GED) is a flexible measure of dissimilarity between graphs which arises in error-correcting graph matching. It is defined from an optimal sequence of edit operations (edit path) transforming one graph into another. Unfortunately, the exact computation of this measure is NP-hard. In the last decade, several approaches were proposed to approximate the GED in polynomial time, mainly by solving linear programming problems. Among them, the bipartite GED received much attention. It is deduced from a linear sum assignment of the nodes of the two graphs, which can be efficiently computed by Hungarian-type algorithms. However, edit operations on nodes and edges are not handled simultaneously, which limits the accuracy of the approximation. To overcome this limitation, we propose to extend the linear assignment model to a quadratic one. This is achieved through the definition of a family of edit paths induced by assignments between nodes. We formally show that the GED, restricted to the paths in this family, is equivalent to a quadratic assignment problem. Since this problem is NP-hard, we propose to compute an approximate solution by adapting two algorithms: Integer Projected Fixed Point method and Graduated Non Convexity and Concavity Procedure. Experiments show that the proposed approach is generally able to reach a more accurate approximation of the exact GED than the bipartite GED, with a computational cost that is still affordable for graphs of non trivial sizes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Graph edit distance Edit paths Quadratic assignment problem Combinatorial optimization Relaxation methods

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DISPLACEMENT pattern-MATCHING AND BOUNDARY-ELEMENT METHODS FOR ELASTIC-PLASTIC STRESS-ANALYSIS

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EXPERIMENTAL MECHANICS 1990年第1期30卷 26-33页

作者： SIRKIS, JS TAYLOR, CE UNIV FLORIDA DEPT AEROSP ENGN MECH & ENGN SCI GAINESVILLE FL 32611 USA

A two-dimensional hybrid experimental-numerical technique for elastic-plastic stress analysis is presented. This technique results from merging two relatively new technologies in engineering mechanics: boundary-element methods and image processing. A syntactic pattern recognition scheme, termed ‘displacement pattern matching’ (DPM), determines the displacement boundary conditions which are used in an elasticplastic boundary-element method (EPBEM) code. The result is an automated stress-analysis tool. Displacement pattern matching is a process where displacements are measured by tracking an arbitrary array of ‘black’ spots on a ‘white’ specimen. The digitized images of the specimen are compared in a double-exposure format to determine displacements. Displacement pattern matching is a full-field technique, with spatial resolution on the order of. 1 pixels. Displacement pattern matching supplies the actual specimen displacement increments to the EPBEM code which is based on a von Mises, isotropic work-hardening constitutive model. Given these displacements and free surface conditions, EPBEM is able to incrementally calculate the internal state of stress at selected locations. Results obtained for a variety of geometries and loading conditions compared well with ANSYS finite-element solutions and selected published experimental solutions and therefore are encouraging.

关键词： pattern matching ELASTOPLASTIC ANALYSIS structural pattern recognition Engineering mechanics Boundary conditions Job Ladders Dislocated Workers Stress analysis DISPLACEMENT Elastoplasticity Displacement (Defense Mechanism) displacement

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Intelligent methods for data retrieval in fusion databases

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FUSION ENGINEERING AND DESIGN 2008年第2-3期83卷 382-386页

作者： Vega, J. Culham Sci Ctr JET EFDA Abingdon OX14 3DB Oxon England EURATOM CIEMAT Fus Madrid 28040 Spain

The plasma behaviour is identified through the recognition of patterns inside signals. The search for patterns is usually a manual and tedious procedure in which signals need to be examined individually. A breakthrough in data retrieval for fusion databases is the development of intelligent methods to search for patterns. A pattern (in the broadest sense) could be a single segment of a waveform, a set of pixels within an image or even a heterogeneous set of features made up of waveforms, images and any kind of experimental data. Intelligent methods will allow searching for data according to technical, scientific and structural criteria instead of an identifiable time interval or pulse number. Such search algorithms should be intelligent enough to avoid passing over the entire database. Benefits of such access methods are discussed and several available techniques are reviewed. In addition, the applicability of the methods from general purpose searching systems to ad hoc developments is covered. (c) 2007 Elsevier B.V. All rights reserved.

关键词： data mining information retrieval image processing nuclear fusion structural pattern recognition

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