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检索条件"任意字段=7th International Workshop on Graph-Based Representations in Pattern Recognition"

共 197 条记录，以下是71-80 订阅

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A comparison of explicit and implicit graph embedding methods for pattern recognition

A comparison of explicit and implicit graph embedding method...

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： Conte, Donatello Ramel, Jean-Yves Sidère, Nicolas Luqman, Muhammad Muzzamil Gaüzère, Benoît Gibert, Jaume Brun, Luc Vento, Mario Italy 64 Avenue Jean Portalis 37200 Tours France L3i Laboratory University of la Rochelle 17042 La Rochelle France GREYC UMR CNRS 6072 14000 Caen France

ISBN: (纸本)9783642382208

In recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computational expensive graph based representations to benefit from mature, less expensive and efficient state of the art machine learning models of statistical pattern recognition. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification. © 2013 Springer-Verlag.

关键词： Classification (of information)

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On the evaluation of graph centrality for shape matching

On the evaluation of graph centrality for shape matching

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose input feature vector consists of the measurements obtained by the centralities and evaluate the different performances for each centrality. © 2013 Springer-Verlag.

关键词： graphic methods

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An algorithm for maximum common subgraph of planar triangulation graphs

An algorithm for maximum common subgraph of planar triangula...

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： Lu, Yao Bunke, Horst Liu, Cheng-Lin National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences China University of Bern Switzerland

ISBN: (纸本)9783642382208

We propose a new fast algorithm for solving the Maximum Common Subgraph (MCS) problem. MCS is an NP-complete problem. In this paper, we focus on a special class of graphs, i.e. Planar Triangulation graphs, which are commonly used in computer vision, pattern recognition and graphics. By exploiting the properties of Planar Triangulation graphs and restricting the problem to connected MCS, for two such graphs of size n and m and their maximum common subgraph of size k, our algorithm solves the MCS problem approximately with time complexity O(nmk). © 2013 Springer-Verlag.

关键词： pattern recognition

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A fast matching algorithm for graph-based handwriting recognition

A fast matching algorithm for graph-based handwriting recogn...

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： Fischer, Andreas Suen, Ching Y. Frinken, Volkmar Riesen, Kaspar Bunke, Horst Centre for Pattern Recognition and Machine Intelligence Concordia University 1455 de Maisonneuve Blvd West Montreal QC H3G 1M8 Canada Computer Vision Center Dept. of Computer Science Universitat Autònoma de Barcelona 08193 Bellaterra Spain Institute for Informations Systems University of Applied Sciences and Arts Northwestern Switzerland Riggenbachstrasse 16 4600 Olten Switzerland Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 3012 Bern Switzerland

ISBN: (纸本)9783642382208

the recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. this approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. the time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. © 2013 Springer-Verlag.

关键词： Vector spaces

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graph-based representations in pattern recognition - 8th IAPR-TC-15 international workshop, GbRPR 2011, Proceedings

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8th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2011

ISBN: (纸本)9783642208430

the proceedings contain 34 papers. the topics discussed include: a global method for reducing multidimensional size graphs;graph descriptors from B-matrix representation;dimensionality reduction for graph of words embedding;entropy versus heterogeneity for graphs;learning generative graph prototypes using simplified von Neumann entropy;information-geometric graph indexing from bags of partial node coverages;maximum likelihood for Gaussians on graphs;towards performance evaluation of graph-based representation;measuring the distance of generalized maps;aggregated search in graph databases: preliminary results;speeding up graph edit distance computation through fast bipartite matching;two new graph kernels and applications to chemoinformatics;parallel graduated assignment algorithm for multiple graph matching based on a common labeling;and smooth simultaneous structural graph matching and point-set registration.

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Estimation of distribution algorithm for the Max-Cut problem

Estimation of distribution algorithm for the Max-Cut problem

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Haxhimusa, Yll Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

In this paper, we investigate the Max-Cut problem and propose a probabilistic heuristic to address its classic and weighted version. Our approach is based on the Estimation of Distribution Algorithm (EDA) that creates a population of individuals capable of evolving at each generation towards the global solution. We have applied the Max-Cut problem for image segmentation and defined the edges' weights as a modified function of the L2 norm between the RGB values of nodes. the main goal of this paper is to introduce a heuristic for Max-Cut and additionally to investigate how it can be applied in the segmentation context. © 2013 Springer-Verlag.

关键词： Image segmentation

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8th Iapr-tc-15 international workshop on graph-based representations in pattern recognition, Gbrpr 2011

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8th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2011

ISBN: (纸本)9783642208430

the proceedings contain 34 papers. the special focus in this conference is on graph-based representations in pattern recognition. the topics include: Speeding Up graph Edit Distance Computation through Fast Bipartite Matching;two New graph Kernels and Applications to Chemoinformatics;generalized Learning graph Quantization;parallel Graduated Assignment Algorithm for Multiple graph Matching based on a Common Labelling;smooth Simultaneous Structural graph Matching and Point-Set Registration;automatic Learning of Edit Costs based on Interactive and Adaptive graph recognition;exploration of the Labelling Space Given graph Edit Distance Costs;graph Matching based on Dot Product Representation of graphs;indexing with Well-Founded Total Order for Faster Subgraph Isomorphism Detection;graph Descriptors from B-Matrix Representation;graph Transduction as a Non-cooperative Game;a graph-based Approach to Feature Selection;spatio-Temporal Extraction of Articulated Models in a graph Pyramid;semi-supervised Segmentation of 3D Surfaces Using a Weighted graph Representation;convexity Grouping of Salient Contours;hierarchical Interactive Image Segmentation Using Irregular Pyramids;tiled Top-Down Pyramids and Segmentation of Large Histological Images;segmentation of Similar Images Using graph Matching and Community Detection;automatic Street graph Construction in Sketch Maps;people Re-identification by graph Kernels Methods;dimensionality Reduction for graph of Words Embedding;automatic Labeling of Handwritten Mathematical Symbols via Expression Matching;structure-based Evaluation Methodology for Curvilinear Structure Detection Algorithms;keygraphs for Sign Detection in Indoor Environments by Mobile Phones;classification of graph Sequences Utilizing the Eigenvalues of the Distance Matrices and Hidden Markov Models;using Kernels on Hierarchical graphs in Automatic Classification of Designs;entropy versus Heterogeneity for graphs.

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Relational Change pattern Mining based on Modularity Difference

Relational Change Pattern Mining Based on Modularity Differe...

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7th Multi-Disciplinary international workshop on Artificial Intelligence (MIWAI)

作者： Okubo, Yoshiaki Haraguchi, Makoto Tomita, Etsuji Hokkaido Univ Grad Sch Informat Sci & Technol N-14 W-9 Sapporo Hokkaido 0600814 Japan Univ Electro Commun Adv Algorithms Res Lab Chofu Tokyo 1828585 Japan

ISBN: (纸本)9783642449499;9783642449482

this paper is concerned with a problem of detecting relational changes. Many kinds of graph data including social networks are increasing nowadays. In such a graph, the relationships among vertices are changing day by day. therefore, it would be worth investigating a data mining method for detecting significant patterns informing us about what changes. We present in this paper a general framework for detecting relational changes over two graphs to be contrasted. Our target pattern with relational change is defined as a set of vertices common in both graphs in which the vertices are almost disconnected in one graph, while densely connected in the other. We formalize such a target pattern based on the notions of modularity and k-plex. A depth-first algorithm for the mining task is designed as an extension of k-plex enumerators with some pruning mechanisms. Our experimental results show usefulness of the proposed method for two pairs of graphs representing actual reply-communications among Twitter users and word co-occurrence relations in Japanese news articles.

关键词： graph theory

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GMTE: A tool for graph transformation and exact/inexact graph matching

GMTE: A tool for graph transformation and exact/inexact grap...

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9th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2013

作者： Hannachi, Mohamed Amine Bouassida Rodriguez, Ismael Drira, Khalil Pomares Hernandez, Saul Eduardo CNRS LAAS 7 avenue du colonel Roche F-31400 Toulouse France Univ. de Toulouse LAAS F-31400 Toulouse France ReDCAD University of Sfax B.P. 1173 3038 Sfax Tunisia Computer Science Department Instituto Nacional de Astrofísica Óptica Y Electrónica C.P. 72840 Tonantzintla Puebla Mexico

ISBN: (纸本)9783642382208

Multi-labelled graphs are a powerful and versatile tool for modelling real applications in diverse domains such as communication networks, social networks, and autonomic systems, among others. Due to dynamic nature of such kind of systems the structure of entities is continuously changing along the time, this because, it is possible that new entities join the system, some of them leave it or simply because the entities' relations change. Here is where graph transformation takes an important role in order to model systems with dynamic and/or evolutive configurations. graph transformation consists of two main tasks: graph matching and graph rewriting. At present, few graph transformation tools support multi-labelled graphs. To our knowledge, there is no tool that support inexact graph matching for the purpose of graph transformation. Also, the main problem of these tools lies on the limited expressiveness of rewriting rules used, that negatively reduces the range of application scenarios to be modelling and/or negatively increase the number of rewriting rules to be used. In this paper, we present the tool GMTE - graph Matching and Transformation Engine. GMTE handles directed and multi-labelled graphs. In addition, to the exact graph matching, GMTE handles the inexact graph matching. the approach of rewriting rules used by GMTE combines Single PushOut rewriting rules with edNCE grammar. this combination enriches and extends the expressiveness of the graph rewriting rules. In addition, for the graph matching, GMTE uses a conditional rule schemata that supports complex comparison functions over labels. To our knowledge, GMTE is the first graph transformation tool that offers such capabilities. © 2013 Springer-Verlag.

关键词： Functions

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Feature space reduction for graph-based image classification

Feature space reduction for graph-based image classification

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18th Iberoamerican Congress on pattern recognition, CIARP 2013

作者： Acosta-Mendoza, Niusvel Gago-Alonso, Andrés Carrasco-Ochoa, Jesús Ariel Martínez-Trinidad, José Francisco Medina-Pagola, José E. 7a # 21406 e/ 214 and 216 Siboney Playa CP: 12200 Havana Cuba Luis Enrique Erro No. 1 Sta. María Tonantzintla Puebla CP: 72840 Mexico

ISBN: (纸本)9783642418211

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. this is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features. © Springer-Verlag 2013.

关键词： Feature Selection

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