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.
the proceedings contain 17 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Spectral graph Sample Weighting for Interpretable Sub-cohort Analysis in ...
ISBN:
(纸本)9783031745607
the proceedings contain 17 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Spectral graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging;RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence;gene-to-Image: Decoding Brain Images from Genetics via Latent Diffusion Models;physics-Guided Multi-view graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling;automated Patient-Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy;MNA-net: Multimodal Neuroimaging Attention-based Architecture for Cognitive Decline Prediction;Improving Brain MRI Segmentation with Multi-Stage Deep Domain Unlearning;DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity;Strongly Topology-Preserving GNNs for Brain graph Super-Resolution;generative Hypergraph Neural Network for Multiview Brain Connectivity Fusion;identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging Data Stratified by Sex and Validated Against Dementia-Related Risk Factors;integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction;self-Supervised Contrastive Learning for Consistent Few-Shot Image representations;Neurocognitive Latent Space Regularization for Multi-label Diagnosis from MRI;Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study.
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.
the collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process. We are interested in automatically f...
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ISBN:
(纸本)9783642021237
the collection of behavior protocols is a common practice in human factors research, but the analysis of these large data sets has always been a tedious and time-consuming process. We are interested in automatically finding canonical behaviors: a small subset of behavioral protocols that is most representative of the full data set, providing a view of the data with as few protocols as possible. Behavior protocols often have a natural graph-based representation, yet there has been little work applying graphtheory to their study. In this paper we extend our recent algorithm by taking into account the graph topology induced by the paths taken through the space of possible behaviors. We applied this technique to find canonical web-browsing behaviors for computer users. By comparing identified canonical sets to a ground truth determined by expert human coders. we found that this graph-based metric outperforms our previous metric based on edit distance.
Structural patternrecognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Coh...
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ISBN:
(纸本)9783642021237
Structural patternrecognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space that does homology. It assigns 'quantities' to the chains used in homology to characterize holes of any dimension. graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. this paper presents cohomology in the context of structural patternrecognition and introduces an algorithm to efficiency compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. Extension to nD and application in the context of patternrecognition are discussed.
Matrix representations for graphs play an important role in combinatorics. In this paper, we investigate four matrix representations for graphs and carry out an characteristic polynomial analysis upon them. the first ...
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ISBN:
(纸本)9783642021237
Matrix representations for graphs play an important role in combinatorics. In this paper, we investigate four matrix representations for graphs and carry out an characteristic polynomial analysis upon them. the first two graph matrices are the adjacency matrix and Laplacian matrix. these two matrices call be obtained straightforwardly from graphs. the second two matrix representations, which are newly introduced [9][3], arc closely related withthe Ihara zeta function and the discrete time quantum walk. they have a similar form and are established from a transformed graph. i.e. the oriented line graph of the original graph. We make use of the characteristic polynomial coefficients of the four matrices to characterize graphs and construct pattern spaces with a fixed dimensionality. Experimental results indicate that the two matrices in the transformed domain perform better than the two matrices in the original graph domain whereas the matrix associated withthe Ihara zeta function is more efficient and effective than the matrix associated withthe discrete time quantum walk and the remaining methods.
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.
graphs and graph transformation are versatile tools for representing and interpreting the contents of document images. three main components are involved: a graph representing the contents of a document image at some ...
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ISBN:
(纸本)3540252703
graphs and graph transformation are versatile tools for representing and interpreting the contents of document images. three main components are involved: a graph representing the contents of a document image at some level of interpretation, a set of graph transformation rules (graph productions), and a mechanism for controlling the application of the graph productions. We review existing document analysis systems that use graph transformation, and discuss challenges and research opportunities in this area.
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.
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