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Structural, Syntactic, and Statistical Pattern Recognition : Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings / edited by Xiao Bai, Edwin R. Hancock, Tin Kam Ho, Richard C. Wilson, Battista Biggio, Antonio Robles-Kelly.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Contributor:
Xiao, Bai, Editor.
Hancock, Edwin R., Editor.
Ho, Tin Kam, Editor.
Wilson, Richard C., Editor.
Biggio, Battista, Editor.
Robles-Kelly, Antonio., Editor.
SpringerLink (Online service)
Series:
Computer Science (SpringerNature-11645)
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11004
Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11004
Language:
English
Subjects (All):
Artificial intelligence.
Pattern recognition systems.
Computer vision.
Algorithms.
Computer science-Mathematics.
Discrete mathematics.
Artificial intelligence-Data processing.
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Discrete Mathematics in Computer Science.
Data Science.
Local Subjects:
Artificial Intelligence.
Automated Pattern Recognition.
Computer Vision.
Algorithms.
Discrete Mathematics in Computer Science.
Data Science.
Physical Description:
1 online resource (XIII, 524 pages) : 134 illustrations
Edition:
1st ed. 2018.
Contained In:
Springer Nature eBook
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2018.
System Details:
text file PDF
Summary:
This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods. .
Contents:
Classification and Clustering
Image annotation using a semantic hierarchy
Malignant Brain Tumor Classification using the Random Forest Method
Rotationally Invariant Bark Recognition
Dynamic voting in multi-view learning for radiomics applications
Iterative Deep Subspace Clustering
A scalable spectral clustering algorithm based on landmark-embedding and cosine similarity
Deep Learning and Neural Networks
On Fast Sample Preselection for Speeding up Convolutional Neural Network Training
UAV First View Landmark Localization via Deep Reinforcement Learning
Context Free Band Reduction Using a Convolutional Neural Network
Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks
Learning Deep Embeddings via Margin-based Discriminate Loss
Dissimilarity Representations and Gaussian Processes
Protein Remote Homology Detection using Dissimilarity-based Multiple Instance Learning
Local Binary Patterns based on Subspace Representation of Image Patch for Face Recognition
An image-based representation for graph classification
Visual Tracking via Patch-based Absorbing Markov Chain
Gradient Descent for Gaussian Processes Variance Reduction
Semi and Fully Supervised Learning Methods
Sparsification of Indefinite Learning Models
Semi-supervised Clustering Framework Based on Active Learning for Real Data
Supervised Classification Using Feature Space Partitioning
Deep Homography Estimation with Pairwise Invertibility Constraint
Spatio-temporal Pattern Recognition and Shape Analysis
Graph Time Series Analysis using Transfer Entropy
Analyzing Time Series from Chinese Financial Market Using A Linear-Time Graph Kernel
A Preliminary Survey of Analyzing Dynamic Time-varying Financial Networks Using Graph Kernels
Few-Example Affine Invariant Ear Detection in the Wild
Line Voronoi Diagram using Elliptical Distances
Structural Matching
Modelling the Generalised Median Correspondence through an Edit Distance
Learning the Graph Edit Distance edit costs based on an embedded model
Ring Based Approximation of Graph Edit Distance
Graph Edit Distance in the exact context
The VF3-Light Subgraph Isomorphism Algorithm: when doing less is more effective
A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance
Error-Tolerant Geometric Graph Similarity
Learning Cost Functions for Graph Matching
Multimedia Analysis and Understanding
Matrix Regression-based Classification for Face Recognition
Plenoptic Imaging for Seeing Through Turbulence
Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions
Cross-model Retrieval with Reconstruct Hashing
Deep Supervised Hashing with Information Loss
Single Image Super Resolution via Neighbor Reconstruction
An Efficient Method for Boundary Detection from Hyperspectral Imagery
Graph-Theoretic Methods
Bags of Graphs for Human Action Recognition
Categorization of RNA Molecules using Graph Methods
Quantum Edge Entropy for Alzheimer's Disease Analysis
Approximating GED using a Stochastic Generator and Multistart IPFP
Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks
On Association Graph Techniques for Hypergraph Matching
Directed Network Analysis using Transfer Entropy Component Analysis
A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs
Dirichlet Densifiers: Beyond Constraining the Spectral Gap.
Other Format:
Printed edition:
ISBN:
978-3-319-97785-0
9783319977850
Access Restriction:
Restricted for use by site license.

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