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Structural, Syntactic, and Statistical Pattern Recognition

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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.


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.


Titel: Structural, Syntactic, and Statistical Pattern Recognition
Untertitel: Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018, Proceedings
EAN: 9783319977850
Digitaler Kopierschutz: Wasserzeichen
Format: E-Book (pdf)
Hersteller: Springer International Publishing
Genre: IT & Internet
Anzahl Seiten: 524
Veröffentlichung: 10.08.2018
Dateigrösse: 37.2 MB

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