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Minimizing the topological structure of line images 7th

7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Kropatsch, Walter G. Burge, Mark Vienna University of Technology Institute of Automation 183/2 Pattern Recognition and Image Processing Group Treitlstr.3 WienA-1040 Austria Johannes Kepler University Institute of Systems Science Computer Vision Laboratory LinzA-4040 Austria

ISBN: (纸本)3540648585

We present a new algorithm based on Dual graph Contraction (DGC) to transform the Run graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) steps. A MLPP graph of a line image compliments the structural information in geometric graph representations like the run graph. Using such a graph and its dual, line image analysis systems can efficiently detect topological features like loops and holes and make use of relations like containment. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Topology

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graph embedding in vector spaces by means of prototype selection

Graph embedding in vector spaces by means of prototype selec...

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6th international workshop on graph-based representations in pattern recognition

作者： Riesen, Kaspar Neuhaus, Michel Bunke, Horst Univ Bern Dept Comp Sci Neubruckstr 10 CH-3012 Bern Switzerland

ISBN: (纸本)9783540729020

the field of statistical pattern recognition is characterized by the use of feature vectors for pattern representation, while strings or, more generally, graphs are prevailing in structural pattern recognition. In this paper we aim at bridging the gap between the domain of feature based and graph based object representation. We propose a general approach for transforming graphs into n-dimensional real vector spaces by means of prototype selection and graph edit distance computation. this method establishes the access to the wide range of procedures based on feature vectors without loosing the representational power of graphs. through various experimental results we show that the proposed method, using graph embedding and classification in a vector space, outperforms the tradional approach based on k-nearest neighbor classification in the graph domain.

关键词： pattern recognition

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Image Segmentation Using graph representations and Local Appearance and Shape Models

Image Segmentation Using Graph Representations and Local App...

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7th international workshop on graph-based representations in pattern recognition

作者： Keustermans, Johannes Seghers, Dieter Mollemans, Wouter Vandermeulen, Dirk Suetens, Paul Katholieke Univ Leuven Fac Med Med Imaging Res Ctr Radiol ESAT PSI Univ Hosp Gasthuisberg Herestr 49 B-3000 Louvain Belgium Medicim NV B-2800 Mechelen Belgium

ISBN: (纸本)9783642021237

A generic model-based segmentation algorithm is presented. based on a set of training data, consisting of images with corresponding object segmentations, a local appearance and local shape model is build. the object is described by a set of landmarks. For each landmark a local appearance model is build. this model describes the local intensity values in the image around each landmark. the local shape model is constructed by considering the landmarks to be vertices in an undirected graph. the edges represent the relations between neighboring landmarks. By implying the markovianity property on the graph, every landmark is only directly dependent upon its neighboring landmarks, leading to a local shape model. the objective function to be minimized is obtained from a maximum a-posteriori approach. To minimize this objective function, the problem is discretized by considering a finite set of possible candidates for each landmark. In this way the segmentation problem is turned into a labeling problem. Mean field annealing is used to optimize this labeling problem. the, algorithm is validated for the segmentation of teeth from cone beam computed tomography images and for automated cephalometric analysis.

关键词： Image segmentation

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Optimising the the Volgenant-Jonker algorithm for approximating graph edit distance

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pattern recognition LETTERS 2017年 87卷 47-54页

作者： Jones, William Chawdhary, Aziem King, Andy Univ Kent Sch Comp Canterbury CT2 7NF Kent England

Although it is agreed that the Volgenant-Jonker (VJ) algorithm provides a fast way to approximate graph edit distance (GED), until now nobody has reported how the VJ algorithm can be tuned for this task. To this end, we revisit VJ and propose a series of refinements that improve both the speed and memory footprint without sacrificing accuracy in the GED approximation. We quantify the effectiveness of these optimisations by measuring distortion between control-flow graphs: a problem that arises in malware matching, We also document an unexpected behavioural property of VJ ill which the time required to find shortest paths to unassigned vertices decreases as graph size increases, and explain how this phenomenon relates to the birthday paradox. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Attributed graphs graph edit distance Volgenate-Jonker algorithm

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Asymmetric inexact matching of spatially-attributed graphs

Asymmetric inexact matching of spatially-attributed graphs

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5th international workshop on graph-based representations in pattern recognition

作者： Li, Y Blostein, D Abolmaesumi, P Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)3540252703

this paper discusses inexact matching of graphs that are spatially-attributed and asymmetric. In a spatially-attributed graph, vertex attributes indicate the coordinates of an image feature represented by the vertex. Asymmetry arises when two graphs represent the same data at different resolutions: this causes an edge in the coarse graph to match an entire path in the fine graph. the two graphs may use different coordinate systems, so a coordinate transform must be estimated during the graph matching. We present an edge first graph matching algorithm to solve this problem, and illustrate its application to the registration of satellite images to road maps. In our current implementation, graphs that represent road networks are manually extracted from satellite images and digitized road maps. Most of the existing algorithms are not designed to handle the asymmetry present when matching a coarse graph to a fine graph.

关键词： graph theory

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Data-driven Latent graph Structure Learning for Diagnosis of Alzheimer's Syndrome 26

Data-driven Latent Graph Structure Learning for Diagnosis of...

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Wang, Jianjia Wu, Chong Shanghai Univ Shanghai Inst Adv Commun & Data Sci Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Complex systems often have a latent graph structure. Studying the underlying graph structure will help us to analyze the mechanisms of complex phenomena. However, it is a challenging problem to learn effective graph structures from the data and apply them to downstream tasks. In this paper, we propose an end-to-end graph learning approach for Alzheimer's syndrome diagnosis based on functional magnetic resonance imaging (fMRI) data of brain regions, which is completely data-driven. the interactions between time-series of each brain region are represented as graph structures, and a multi-head attention mechanism is used to update the representations of the nodes. then, the graph structures are obtained from the feature sampling of the edges. Finally, the learned graph structure is combined with the left-out time-series data features and the node prior to completing the classification task of the brain network. In comparison with the latest research methods, our approach achieves higher classification accuracy.

关键词： Image edge detection Functional magnetic resonance imaging pattern recognition Task analysis Alzheimer's disease Complex systems

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Optimum-Path Forest based on k-connectivity: theory and applications

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pattern recognition LETTERS 2017年第Feb.1期87卷 117-126页

作者： Papa, Joao Paulo Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube BR-17033360 Bauru SP Brazil Univ Fed Sao Carlos Dept Comp Rod Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil Univ Estadual Campinas Inst Comp Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. the experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.

关键词： pattern classification Optimum-Path Forest Supervised learning

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graph-based representations in pattern recognition 1

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丛书名： Lecture Notes in Computer Science

1000年

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

ISBN: (数字)9783319182247

ISBN: (纸本)9783319182230

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.

关键词： pattern recognition Image Processing and Computer Vision Computer graphics Discrete Mathematics in Computer Science Data Structures Algorithm Analysis and Problem Complexity

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A Survey on Applications of Bipartite graph Edit Distance 11th

A Survey on Applications of Bipartite Graph Edit Distance

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11th IAPR-TC15 international workshop on graph-based representations in pattern recognition (GbRPR)

作者： Stauffer, Michael Tschachtli, thomas Fischer, Andreas Riesen, Kaspar Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Riggenbachstr 16 CH-4600 Olten Switzerland Univ Fribourg Dept Informat CH-1700 Fribourg Switzerland Univ Appl Sci & Arts Western Switzerland Inst Complex Syst CH-1705 Fribourg Switzerland Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9783319589619;9783319589602

About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. this particular framework allows the approximation of graph edit distance in cubic time. this, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. the present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).

关键词： Applications of bipartite graph matching graph-based pattern representations

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Local Binary pattern based graph Construction for Hyperspectral Image Segmentation 1

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12th IAPR-TC15 workshop on graph-based representations in pattern recognition (GbRPR)

作者： Tabia, Kaouther Desquesnes, Xavier Lucas, Yves Treuillet, Sylvie Univ Orleans Lab PRISME 12 Rue Blois 6744 F-45067 Orleans 2 France

ISBN: (数字)9783030200817

ISBN: (纸本)9783030200817;9783030200800

Building highly discriminative graph has an important impact on the quality of graph-based hyperspectral image segmentation. For this purpose, we propose to weight graph edges using Local Binary pattern (LBP) descriptor that takes into account the texture information of the hyperspectral images. Nodes in the graph embed spectral LBP features computed from the different hyperspectral bands, while edges encode the spatial relationship between these features. the multiphase level set method is then applied on the constructed graph to segment the image. We validate the proposed method, using Overlapping Score evaluation metric, on several popular hyperspectral images. the results show that our method is very efficient compared to other state-of-the-art one.

关键词： Hyperspectral image graph LBP Segmentation Multiphase level set

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