the proceedings contain 23 papers. the special focus in this conference is on Data Structures and Representation. the topics include: Construction of combinatorial pyramids;on graphs with unique node labels;maximal in...
ISBN:
(纸本)354040452X
the proceedings contain 23 papers. the special focus in this conference is on Data Structures and Representation. the topics include: Construction of combinatorial pyramids;on graphs with unique node labels;maximal independent directed edge set;functional modeling of structured images;building of symbolic hierarchical graphs for feature extraction;comparison and convergence of two topological models for 3D image segmentation;tree edit distance from information theory;self-organizing graph edit distance;graph edit distance with node splitting and merging, and its application to diatom identification;orthonormal kernel kronecker product graph matching;theoretical analysis and experimental comparison of graph matching algorithms for database filtering;a comparison of three maximum common subgraph algorithms on a large database of labeled graphs;swap strategies for graph matching;graph matching using spectral seriation and string edit distance;graph polynomials, principal pivoting, and maximum independent sets;graph partition for matching;spectral clustering of graphs;comparison of distance measures for graph-based clustering of documents;some experiments on clustering a set of strings;a new median graph algorithm;graph clustering using the weighted minimum common supergraph;ACM attributed graph clustering for learning classes of images and a competitive winner-takes-all architecture for classification and patternrecognition of structures.
Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised withthe CT scan in order to track the movement of the lung. It is routinely us...
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In this paper we try to examine recent trends on the use of graph-basedrepresentations in patternrecognition, using as a vantage point the 11th IAPR-TC15 workshop GbR2017, dedicated to this topic. A survey of the pa...
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In this paper we try to examine recent trends on the use of graph-basedrepresentations in patternrecognition, using as a vantage point the 11th IAPR-TC15 workshop GbR2017, dedicated to this topic. A survey of the paper presented at GbR2017 will give us the opportunity to reflect on the directions where the interest of the research community working on this subject is moving. (C) 2018 Elsevier B.V. All rights reserved.
graph pyramids are often used for representing irregular image pyramids. For the 2D case, combinatorial pyramids have been recently defined in order to explicitly represent more topological information than graph pyra...
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graph pyramids are often used for representing irregular image pyramids. For the 2D case, combinatorial pyramids have been recently defined in order to explicitly represent more topological information than graph pyramids. the main contribution of this work is the definition of pyramids of n-dimensional (nD) generalized maps. this extends the previous works to any dimension, and generalizes them in order to represent any type of pyramid constructed by using any removal and/or contraction operations. We give basic algorithms that allow to build an nD generalized pyramid that describes a multi-level segmented image. A pyramid of nD generalized maps can be implemented in several ways. We propose three possible representations and give conversion algorithms. (c) 2005patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
Irregular pyramids are made of a stack of successively reduced graphs embedded in the plane. Such pyramids are used within the segmentation framework to encode a hierarchy of partitions. the different graph models use...
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Irregular pyramids are made of a stack of successively reduced graphs embedded in the plane. Such pyramids are used within the segmentation framework to encode a hierarchy of partitions. the different graph models used within the irregular pyramid framework encode different types of relationships between regions. this paper compares different graph models used within the irregular pyramid framework according to a set of relationships between regions. We also define a new algorithm based on a pyramid of combinatorial maps which allows to determine if one region contains the other using only local calculus. (c) 2005patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
Algorithms for the analysis of graph sequences are proposed in this paper. In particular, we study the problem of recovering missing information and predicting the Occurrence of nodes and edges in time series of graph...
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Algorithms for the analysis of graph sequences are proposed in this paper. In particular, we study the problem of recovering missing information and predicting the Occurrence of nodes and edges in time series of graphs. Two different recovery schemes are developed. the first scheme uses reference patterns that are extracted from a training set of graph sequences, while the second method is based on decision tree induction. Our work is motivated by applications in Computer network analysis. However, the proposed recovery and prediction schemes are generic and can be applied in other domains as well. (c) 2005patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
In this paper, we discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. Consistency allows us to study the cost function which cont...
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ISBN:
(纸本)3540252703
In this paper, we discuss the notion of consistency in inexact graph matching to be able to correctly determine the optimal solution of the matching problem. Consistency allows us to study the cost function which controls the graph matching process, regardless of the optimization technique that is used. the analysis is performed in the context of change detection in geospatial information. A condition based on the expected graph error is presented which allows to determine the bounds of error tolerance and in this way characterizes acceptable over inacceptable data inconsistencies.
this work discusses the issue of approximation in point set matching. In general, one may have two classes of approximations when tackling a matching problem: (1) an algorithmic approximation which consists in using s...
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this work discusses the issue of approximation in point set matching. In general, one may have two classes of approximations when tackling a matching problem: (1) an algorithmic approximation which consists in using suboptimal procedures to infer the assignment, and (2), a representational approximation which involves a simplified and suboptimal model for the original data. Matching techniques have typically relied on the first approach by retaining the complete model and using suboptimal techniques to solve it. In this paper, we show how a technique based on using exact inference in simple graphical Models, an instance of the second class, can significantly outperform instances of techniques from the first class. We experimentally compare this method with well-known Spectral and Relaxation methods, which are exemplars of the first class. We have performed experiments with synthetic and real-world data sets which reveal significant performance improvement in a wide operating range. (c) 2005patternrecognition Society. Published by Elsevier Ltd. All rights reserved.
graph Neural Networks (GNNs) have achieved state-of-the-art performance on a wide range of graph-based tasks such as graph classification and node classification. this is because the unique structure of GNNs allows th...
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ISBN:
(数字)9781665490627
ISBN:
(纸本)9781665490627
graph Neural Networks (GNNs) have achieved state-of-the-art performance on a wide range of graph-based tasks such as graph classification and node classification. this is because the unique structure of GNNs allows them to effectively learn embeddings for unstructured data. One important operation for graph classification tasks is downsampling or pooling, which obtains graphrepresentations from node representations. However, most GNNs are associated with global pooling, that can not learn hierarchical graphrepresentations. Meanwhile, current hierarchical pooling methods have the shortcomings of unclear node assignment and uniform aggregation. To overcome these drawbacks, we propose an attention-based differentiable pooling operation in this paper, which can learn a hard cluster assignment for nodes and aggregate nodes in each cluster differently by introducing an attention mechanism. Experiments on standard graph classification benchmarks show that our proposed approach performs better when compared with other competing methods.
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