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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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Matching Functional Connectivity patterns for Spatial Correspondence Detection in fMRI Registration

Matching Functional Connectivity Patterns for Spatial Corres...

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6th international workshop on Medical Imaging and Augmented Reality (MIAR) / 8th international workshop on Augmented Reality for Computer- Assisted Interventions (AE-CAI)

作者： Tang, Zhenyu Jiang, Di Li, Hongming Fan, Yong Chinese Acad Sci Inst Automat Brainnetome Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9783642408427;9783642408434

A novel method is proposed to match functional connectivity patterns represented by graphs for spatial registration of fMRI data. Different from existing functional connectivity pattern based registration methods that detect corresponding functional units across different subjects by minimizing their difference in functional connectivity strength, our method adopts a graph representation to characterize functional connectivity information among all voxels in fMRI data of each subject, then detects spatial correspondence between subjects using graph matching. To integrate information of both functional connectivity strength and spatial relations, the graph representation of functional connectivity information of fMRI data models each voxel as one graph node and connects each pair of graph nodes with an edge weighted by their functional connectivity strength measure, estimated as correlation coefficient between their functional signals. To make the graph matching computationally feasible, an iterative matching strategy with stochastic resampling is proposed to match graphs of spatially distributed local functional connectivity patterns and subsequently to drive the image registration iteratively. the proposed method has been validated by registering resting state fMRI data of 20 healthy subjects. the validation experiment results have demonstrated that our method can achieve improved inter-subject functional consistency. A comparison experiment result has further indicated that the proposed method can achieve better performance than existing methods.

关键词： fMRI registration graph matching functional connectivity pattern

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013年 7877 LNCS卷 VI页

作者： Kropatsch, Walter G. Artner, Nicole M. Haxhimusa, Yll Jiang, Xiaoyi Vienna University of Technology Department of Pattern Recognition and Image Processing Favoritenstr. 9-11 186-3 1040 Vienna Austria University of Münster Department of Computer Science Einsteinstr. 62 48149 Münster Germany

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Motion Local Ternary pattern for Distributed Multi-View Human Action recognition

Motion Local Ternary Pattern for Distributed Multi-View Huma...

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6th international Conference on Distributed Smart Cameras (ICDSC)

作者： Luo, Jiajia Qi, Hairong Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781450317726

Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to the single view recognition. In this paper, we propose algorithms for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new method for fast calculation of motion information by Motion Local Ternary pattern (Mltp) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Next, we combine the local interest point detector with Mltp to generate 3D patches containing motion information and introduce two feature descriptors for the extracted 3D patches. Taking advantage of the proposed Mltp, 3D patches generated from background can be further removed automatically and thus the foreground patches can be highlighted. Finally, the histogram representations based on Bag-of-Words modeling, are transmitted from local cameras to the base station for classification. At the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed methods have three advantages: 1) no preprocessing is required;2) communication among cameras is unnecessary;and 3) positions and orientations of cameras do not need to be fixed. We further evaluate both descriptors on the most popular multi-view action dataset IXMAS. Experimental results indicate that our approaches repeatedly achieve state-of-the-art results when various numbers of views are tested. In addition, our approaches are tolerant to the various combination of views and benefit from introducing more views at the testing stage.

关键词： Cameras

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Java bytecode clone detection via relaxation on code fingerprint and Semantic Web reasoning

Java bytecode clone detection via relaxation on code fingerp...

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international workshop on Software Clones (IWSC)

作者： Iman Keivanloo Chanchai K. Roy Juergen Rilling Department of Computer Science Concordia University Canada Department of Computer Science University of Saskatchewan Canada

While finding clones in source code has drawn considerable attention, there has been only very little work in finding similar fragments in binary code and intermediate languages, such as Java bytecode. Some recent studies showed that it is possible to find distinct sets of clone pairs in bytecode representation of source code, which are not always detectable at source code-level. In this paper, we present a bytecode clone detection approach, called SeByte, which exploits the benefits of compilers (the bytecode representation) for detecting a specific type of semantic clones in Java bytecode. SeByte is a hybrid metric-based approach that takes advantage of both, Semantic Web technologies and Set theory. We use a two-step analysis process: (1) pattern matching via Semantic Web querying and reasoning, and (2) Content matching, using Jaccard coefficient for set similarity measurement. Semantic Web-based pattern matching helps us to find method blocks which share similar patterns even in case of extreme dissimilarity (e.g., numerous repetitions or large gaps). Although it leads to high recall, it gives high false positive rate. We thus use the content matching (via Jaccard) to reduce false positive rate by focusing on content semantic resemblance. Our evaluation of four Java systems and five other tools shows that SeByte can detect a large number of semantic clones that are either not detected or supported by source code based clone detectors.

关键词： Cloning Java Semantics Fingerprint recognition pattern matching Semantic Web Cognition

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Speeding Up graph Edit Distance Computation through Fast Bipartite Matching

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

作者： Fankhauser, Stefan Riesen, Kaspar Bunke, Horst Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

ISBN: (纸本)9783642208447

In the field of structural pattern recognition graphs constitute a very common and powerful way of representing objects. the main drawback of graph representations is that the computation of various graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible graph similarity measures is graph edit distance. In this paper we propose a novel approach for the efficient computation of graph edit distance based on bipartite graph matching by means of the Volgenant-Jonker assignment algorithm. Our proposed algorithm provides only suboptimal edit distances, but runs in polynomial time. the reason for its sub-optimality is that edge information is taken into account only in a limited fashion during the process of finding the optimal node assignment between two graphs. In experiments on diverse graph representations we demonstrate a high speed up of our proposed method over a traditional algorithm for graph edit distance computation and over two other sub-optimal approaches that use the Hungarian and Munkres algorithm. Also, we show that classification accuracy remains nearly unaffected by the suboptimal nature of the algorithm.

关键词： pattern matching

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