Graphs for Pattern Recognition

Infeasible Systems of Linear Inequalities

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Author: Damir Gainanov

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3110481065

Category: Mathematics

Page: 158

View: 1737

Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition.

Random Graphs for Statistical Pattern Recognition

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Author: David J. Marchette

Publisher: John Wiley & Sons

ISBN: 9780471722083

Category: Mathematics

Page: 264

View: 1999

A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the firstbook to address the topic of random graphs as it applies tostatistical pattern recognition. Both topics are of vital interestto researchers in various mathematical and statistical fields andhave never before been treated together in one book. The use ofdata random graphs in pattern recognition in clustering andclassification is discussed, and the applications for bothdisciplines are enhanced with new tools for the statistical patternrecognition community. New and interesting applications for randomgraph users are also introduced. This important addition to statistical literaturefeatures: Information that previously has been available only throughscattered journal articles Practical tools and techniques for a wide range of real-worldapplications New perspectives on the relationship between patternrecognition and computational geometry Numerous experimental problems to encourage practicalapplications With its comprehensive coverage of two timely fields, enhancedwith many references and real-world examples, Random Graphs forStatistical Pattern Recognition is a valuable resource forindustry professionals and students alike.

Applied Graph Theory in Computer Vision and Pattern Recognition

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Author: Abraham Kandel,Horst Bunke,Mark Last

Publisher: Springer Science & Business Media

ISBN: 3540680195

Category: Computers

Page: 265

View: 1495

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Graph-Based Representations in Pattern Recognition

5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings

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Author: Luc Brun

Publisher: Springer Science & Business Media

ISBN: 9783540252702

Category: Computers

Page: 384

View: 5191

This book constitutes the refereed proceedings of the 5th IAPR International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2005, held in Poitiers, France in April 2005. The 18 revised full papers and 17 revised poster papers presented were carefully reviewed and selected from 50 submissions. The papers are organized in topical sections on graph representations, graphs and linear representations, combinatorial maps, matching, hierarchical graph abstraction and matching, inexact

Graph Embedding for Pattern Analysis

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Author: Yun Fu,Yunqian Ma

Publisher: Springer Science & Business Media

ISBN: 1461444578

Category: Technology & Engineering

Page: 260

View: 3940

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

Graph-Based Representations in Pattern Recognition

6th IAPR-TC-15 International Workshop, GbRPR 2007, Alicante, Spain, June 11-13, 2007, Proceedings

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Author: Francisco Escolano

Publisher: Springer Science & Business Media

ISBN: 354072902X

Category: Computers

Page: 416

View: 4274

This book constitutes the refereed proceedings of the 6th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2007, held in Alicante, Spain in June 2007. The 23 revised full papers and 14 revised poster papers presented were carefully reviewed and selected from 54 submissions. The papers are organized in topical sections on matching, distances and measures, graph-based segmentation and image processing, graph-based clustering, graph representations, pyramids, combinatorial maps and homologies, as well as graph clustering, embedding and learning.

Graph Based Representations in Pattern Recognition

4th IAPR International Workshop, GbRPR 2003, York, UK, June 30 - July 2, 2003. Proceedings

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Author: Edwin Hancock,Mario Vento

Publisher: Springer

ISBN: 3540450289

Category: Computers

Page: 276

View: 2377

The refereed proceedings of the 4th IAPR International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2003, held in York, UK in June/July 2003. The 23 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on data structures and representation, segmentation, graph edit distance, graph matching, matrix methods, and graph clustering.

Graph-Based Representations in Pattern Recognition

9th IAPR-TC-15 International Workshop, GbRPR 2013, Vienna, Austria, May 15-17, 2013, Proceedings

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Author: Walter Kropatsch,Nicole M. Artner,Yll Haxhimusa,Xiaoyi Jiang

Publisher: Springer

ISBN: 3642382215

Category: Computers

Page: 255

View: 2674

This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2013, held in Vienna, Austria, in May 2013. The 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. They are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.

Structural Pattern Recognition with Graph Edit Distance

Approximation Algorithms and Applications

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Author: Kaspar Riesen

Publisher: Springer

ISBN: 3319272527

Category: Computers

Page: 158

View: 5077

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm; describes a reformulation of GED to a quadratic assignment problem; illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem; reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework; examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time; includes appendices listing the datasets employed for the experimental evaluations discussed in the book.

Applied Pattern Recognition

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Author: Horst Bunke,Abraham Kandel,Mark Last

Publisher: Springer

ISBN: 3540768319

Category: Mathematics

Page: 246

View: 2080

A sharp increase in the computing power of modern computers has triggered the development of powerful algorithms that can analyze complex patterns in large amounts of data within a short time period. Consequently, it has become possible to apply pattern recognition techniques to new tasks. The main goal of this book is to cover some of the latest application domains of pattern recognition while presenting novel techniques that have been developed or customized in those domains.

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Guadalajara, Jalisco, México, November 15-18, 2009. Proceedings

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Author: Eduardo Bayro Corrochano,Jan-Olof Eklundh

Publisher: Springer Science & Business Media

ISBN: 3642102670

Category: Computers

Page: 1082

View: 7929

This book constitutes the refereed proceedings of the 14th Iberoamerican Congress on Pattern Recognition, CIARP 2009, held in Guadalajara, Mexico, in November 2009. The 64 revised full papers presented together with 44 posters were carefully reviewed and selected from 187 submissions. The papers are organized in topical sections on image coding, processing and analysis; segmentation, analysis of shape and texture; geometric image processing and analysis; analysis of signal, speech and language; document processing and recognition; feature extraction, clustering and classification; statistical pattern recognition; neural networks for pattern recognition; computer vision; video segmentation and tracking; robot vision; intelligent remote sensing, imagery research and discovery techniques; intelligent computing for remote sensing imagery; as well as intelligent fusion and classification techniques.

Graph-Based Representations in Pattern Recognition

7th IAPR-TC-15 International Workshop, GbRPR 2009, Venice, Italy, May 26-28, 2009. Proceedings

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Author: Andrea Torsello,Francisco Escolano Ruiz,Luc Brun

Publisher: Springer Science & Business Media

ISBN: 3642021247

Category: Computers

Page: 378

View: 9279

This book constitutes the refereed proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2009, held in Venice, Italy in May 2009. The 37 revised full papers presented were carefully reviewed and selected from 47 submissions. The papers are organized in topical sections on graph-based representation and recognition, graph matching, graph clustering and classification, pyramids, combinatorial maps, and homologies, as well as graph-based segmentation.

Graph-Based Representations in Pattern Recognition

11th IAPR-TC-15 International Workshop, GbRPR 2017, Anacapri, Italy, May 16–18, 2017, Proceedings

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Author: Pasquale Foggia,Cheng-Lin Liu,Mario Vento

Publisher: Springer

ISBN: 331958961X

Category: Computers

Page: 289

View: 9458

This book constitutes the refereed proceedings of the 11th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. The 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.

Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshops, SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 Proceedings

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Author: Ana Fred,Terry Caelli,Robert P.W. Duin,Aurélio Campilho,Dick de Ridder

Publisher: Springer Science & Business Media

ISBN: 3540225706

Category: Computers

Page: 1169

View: 4921

Advances in Pattern Recognition

Joint IAPR International Workshops, SSPR'98 and SPR'98, Sydney, Australia, August 11-13, 1998, Proceedings

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Author: Adnan Amin,Dov Dori,Pavel Pudil,Herbert Freeman

Publisher: Springer Science & Business Media

ISBN: 9783540648581

Category: Computers

Page: 1050

View: 1035

9

Advances in Structural and Syntactical Pattern Recognition

6th International Workshop, SSPR' 96, Leipzig, Germany, August, 20 - 23, 1996, Proceedings

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Author: Germany) International Workshop on Structural and Syntactic Pattern Recognition (6th : 1996 : Leipzig,Petra Perner,Patrick Wang

Publisher: Springer Science & Business Media

ISBN: 9783540615774

Category: Computers

Page: 392

View: 9205

This book constitutes the refereed proceedings of the 6th International Workshop on Structural and Syntactical Pattern Recognition, SSPR '96, held in Leipzig, Germany in August 1996. The 36 revised full papers included together with three invited papers were carefully selected from a total of 52 submissions. The papers are organized in topical sections on grammars and languages; morphology and mathematical approaches to pattern recognition; semantic nets, relational models and graph-based methods; 2D and 3D shape recognition; document image analysis and recognition; and handwritten and printed character recognition.

Graph-Based Representations in Pattern Recognition

10th IAPR-TC-15 International Workshop, GbRPR 2015, Beijing, China, May 13-15, 2015. Proceedings

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Author: Cheng-Lin Liu,Bin Luo,Walter G. Kropatsch,Jian Cheng

Publisher: Springer

ISBN: 3319182242

Category: Computers

Page: 376

View: 7759

This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015, held in Beijing, China, in May 2015. The 36 papers presented in this volume were carefully reviewed and selected from 53 submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching, 7 in graph clustering and classification, and 7 in graph-based applications.

Graph-Based Representations in Pattern Recognition

8th IAPR-TC-15 International Workshop, GbRPR 2011, Münster, Germany, May 18-20, 2011, Proceedings

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Author: Xiaoyi Jiang,Miquel Ferrer,Andrea Torsello

Publisher: Springer

ISBN: 3642208444

Category: Computers

Page: 345

View: 3832

This book constitutes the refereed proceedings of the 8th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2011, held in Münster, Germany, in May 2011. The 34 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on graph-based representation and characterization, graph matching, classification, and querying, graph-based learning, graph-based segmentation, and applications.

Bridging the Gap Between Graph Edit Distance and Kernel Machines

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Author: Michel Neuhaus,Horst Bunke

Publisher: World Scientific

ISBN: 9812770208

Category: Electronic books

Page: 232

View: 5407

In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition of patterns in the graph domain OCo commonly referred to as graph matching. A large number of methods for graph matching have been proposed. Graph edit distance, for instance, defines the dissimilarity of two graphs by the amount of distortion that is needed to transform one graph into the other and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are highly tolerant towards structural errors. The basic idea is to incorporate concepts from graph edit distance into kernel functions, thus combining the flexibility of edit distance-based graph matching with the power of kernel machines for pattern recognition. The authors introduce a collection of novel graph kernels related to edit distance, including diffusion kernels, convolution kernels, and random walk kernels. From an experimental evaluation of a semi-artificial line drawing data set and four real-world data sets consisting of pictures, microscopic images, fingerprints, and molecules, the authors demonstrate that some of the kernel functions in conjunction with support vector machines significantly outperform traditional edit distance-based nearest-neighbor classifiers, both in terms of classification accuracy and running time."

Graph Classification and Clustering Based on Vector Space Embedding

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Author: Kaspar Riesen,Horst Bunke

Publisher: World Scientific

ISBN: 9814465038

Category:

Page: 348

View: 4064

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector. This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time. Contents:Introduction and Basic ConceptsGraph MatchingGraph Edit DistanceGraph DataKernel MethodsGraph Embedding Using DissimilaritiesClassification Experiments with Vector Space Embedded GraphsClustering Experiments with Vector Space Embedded Graphs Readership: Professionals, academics, researchers and students in pattern recognition, machine perception/computer vision and artificial intelligence. Keywords:Structural Pattern Recognition;Graph Embedding;Graph Classification;Prototype Selection;Graph KernelReview: “It is recommended for the data mining community working on graphs.” Mathematical Reviews Key Features:Provides a major breakthrough in bridging the gap between structural and statistical pattern recognition — two fields that have been considered two separate research directions in the pastShows uniquely how graph-based pattern recognition can benefit from all algorithmic tools that have been originally developed for statistical pattern recognition