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)) step...
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the field of statistical patternrecognition is characterized by the use of feature vectors for pattern representation, while strings or, more generally, graphs are prevailing in structural patternrecognition. In thi...
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ISBN:
(纸本)9783540729020
the field of statistical patternrecognition is characterized by the use of feature vectors for pattern representation, while strings or, more generally, graphs are prevailing in structural patternrecognition. In this paper we aim at bridging the gap between the domain of feature based and graphbased 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.
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 i...
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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.
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, ...
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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 boththe 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.
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. ...
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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.
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 s...
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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 withthe left-out time-series data features and the node prior to completing the classification task of the brain network. In comparison withthe latest research methods, our approach achieves higher classification accuracy.
graph-basedpatternrecognition 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 ga...
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graph-basedpatternrecognition 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.
this book constitutes the refereed proceedings of the 10th IAPR-TC-15 internationalworkshop on graph-basedrepresentations in patternrecognition, GbRPR 2015, held in Beijing, China, in May 2015. the 36 papers presen...
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ISBN:
(数字)9783319182247
ISBN:
(纸本)9783319182230
this book constitutes the refereed proceedings of the 10th IAPR-TC-15 internationalworkshop on graph-basedrepresentations in patternrecognition, 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.
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,...
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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).
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) descript...
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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.
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