Graphs for Pattern Recognition

Infeasible Systems of Linear Inequalities

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Author: Damir Gainanov

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3110480301

Category: Mathematics

Page: 158

View: 2978

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.

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: 5771

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

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

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Author: Edwin Hancock,Mario Vento,International Association for Pattern Recognition

Publisher: Springer Science & Business Media

ISBN: 354040452X

Category: Computers

Page: 270

View: 1565

This volume contains the papers presented at the Fourth IAPR Workshop on Graph Based Representations in Pattern Recognition. The workshop was held at the King’s Manor in York, England between 30 June and 2nd July 2003. The previous workshops in the series were held in Lyon, France (1997), Haindorf, Austria (1999), and Ischia, Italy (2001). The city of York provided an interesting venue for the meeting. It has been said that the history of York is the history of England. There have been both Roman and Viking episodes. For instance, Constantine was proclaimed emperor in York. The city has also been a major seat of ecclesiastical power and was also involved in the development of the railways in the nineteenth century. Much of York’s history is evidenced by its buildings, and the King’s Manor is one of the most important and attractive of these. Originally part of the Abbey, after the dissolution of the monasteries by Henry VIII, the building became a center of government for the Tudors and the Stuarts (who stayed here regularly on their journeys between London and Edinburgh), serving as the headquarters of the Council of the North until it was disbanded in 1561. The building became part of the University of York at its foundation in 1963. The papers in the workshop span the topics of representation, segmentation, graph-matching, graph edit-distance, matrix and spectral methods, and gra- clustering.

Random Graphs for Statistical Pattern Recognition

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Author: David J. Marchette

Publisher: John Wiley & Sons

ISBN: 9780471722083

Category: Mathematics

Page: 237

View: 2629

A timely convergence of two widely used disciplines Random Graphs for Statistical Pattern Recognition is the first book to address the topic of random graphs as it applies to statistical pattern recognition. Both topics are of vital interest to researchers in various mathematical and statistical fields and have never before been treated together in one book. The use of data random graphs in pattern recognition in clustering and classification is discussed, and the applications for both disciplines are enhanced with new tools for the statistical pattern recognition community. New and interesting applications for random graph users are also introduced. This important addition to statistical literature features: Information that previously has been available only through scattered journal articles Practical tools and techniques for a wide range of real-world applications New perspectives on the relationship between pattern recognition and computational geometry Numerous experimental problems to encourage practical applications With its comprehensive coverage of two timely fields, enhanced with many references and real-world examples, Random Graphs for Statistical Pattern Recognition is a valuable resource for industry professionals and students alike.

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,Mario Vento

Publisher: Springer Science & Business Media

ISBN: 9783540252702

Category: Computers

Page: 384

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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

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: 9027

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: 8833

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.

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: 8885

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

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 Science & Business Media

ISBN: 3642208436

Category: Computers

Page: 345

View: 3582

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.

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,Mario Vento

Publisher: Springer

ISBN: 3540729038

Category: Computers

Page: 416

View: 7562

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. It covers 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 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: 2746

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

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: 5230

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

Publisher: Springer Science & Business Media

ISBN: 3642102670

Category: Computers

Page: 1082

View: 6499

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 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: 3922

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

Syntactic and Structural Pattern Recognition

Theory and Applications

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Author: Horst Bunke,Alberto Sanfeliu

Publisher: World Scientific

ISBN: 9789971505660

Category: Computers

Page: 554

View: 4158

This book is currently the only one on this subject containing both introductory material and advanced recent research results. It presents, at one end, fundamental concepts and notations developed in syntactic and structural pattern recognition and at the other, reports on the current state of the art with respect to both methodology and applications. In particular, it includes artificial intelligence related techniques, which are likely to become very important in future pattern recognition.The book consists of individual chapters written by different authors. The chapters are grouped into broader subject areas like “Syntactic Representation and Parsing”, “Structural Representation and Matching”, “Learning”, etc. Each chapter is a self-contained presentation of one particular topic. In order to keep the original flavor of each contribution, no efforts were undertaken to unify the different chapters with respect to notation. Naturally, the self-containedness of the individual chapters results in some redundancy. However, we believe that this handicap is compensated by the fact that each contribution can be read individually without prior study of the preceding chapters. A unification of the spectrum of material covered by the individual chapters is provided by the subject and author index included at the end of the book.

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: 8625

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.

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

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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 G. Kropatsch,Nicole M. Artner,Yll Haxhimusa,Xiaoyi Jiang

Publisher: Springer

ISBN: 3642382215

Category: Computers

Page: 255

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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.

Data Structures, Computer Graphics, and Pattern Recognition

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Author: A. Klinger,K. S. Fu,T. L. Kunii

Publisher: Academic Press

ISBN: 1483267253

Category: Reference

Page: 512

View: 7986

Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology. This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer graphics, and observations on linguistics for scene analysis are also elaborated. This text likewise covers the design of satellite graphics systems, interactive image segmentation, surface representation for computer aided design, and error-correcting parsing for syntactic pattern recognition. This publication is valuable to practitioners in data structures, particularly those who are applying real computer systems to problems involving image, speech, and medical data.