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Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops, S+SSPR 2022, Montreal, QC, Canada, August 26-27, 2022, Proceedings / Adam Krzyzak, Ching Y. Suen, Andrea Torsello, Nicola Nobile (eds.)

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and se...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Author: International Workshop on Structural and Syntactic Pattern Recognition Montréal, Québec
Other Authors: Krzyzak, Adam (Editor), Suen, Ching Y. (Editor), Torsello, Andrea (Editor), Nobile, Nicola (Editor)
Other title:S+SSPR 2022.
Format: Conference Proceeding eBook
Language:English
Published: Cham, Switzerland : Springer, 2022.
Series:Lecture notes in computer science ; 13813.
Subjects:
Pattern recognition systems > Congresses.
Computer vision > Congresses.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Realization of Autoencoders by Kernel Methods
  • 1 Introduction
  • 2 Related Work
  • 3 Autoencoders by Kernel Methods
  • 3.1 Encoder and Decoder
  • 3.2 Fundamental Mapping Without Loss
  • 3.3 Kernelized Autoencoder
  • 4 Comparison with Neural Networks
  • 5 Applications
  • 5.1 Denoising Autoencoders
  • 5.2 Generative Autoencoders
  • 6 Discussion
  • 7 Conclusion
  • References
  • Maximal Independent Vertex Set Applied to Graph Pooling
  • 1 Introduction
  • 2 Related Work
  • 2.1 Graph Pooling
  • 3 Proposed Method.
  • 3.1 Maximal Independent Vertex Set (MIVS)
  • 3.2 Adaptation of MIVS to Deep Learning
  • 4 Experiments
  • 4.1 Datasets
  • 4.2 Model Architecture and Training Procedure
  • 4.3 Ablation Studies
  • 4.4 Comparison of MIVSPool According to Other Methods
  • 5 Conclusion
  • References
  • Annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching
  • 1 Introduction
  • 2 Kieu Database
  • 3 Annotation-Free Keyword Spotting (KWS)
  • 3.1 Synthetic Dataset Creation
  • 3.2 Character Detection
  • 3.3 Graph Extraction
  • 3.4 Graph Matching
  • 3.5 Keyword Spotting (KWS)
  • 4 Experimental Evaluation
  • 4.1 Task Setup and Parameter Optimization
  • 4.2 Results
  • 4.3 Ablation Study
  • 5 Conclusions
  • References
  • Interactive Generalized Dirichlet Mixture Allocation Model
  • 1 Introduction
  • 2 Model Description
  • 3 Variational Inference
  • 4 Interactive Learning Algorithm
  • 5 Experimental Results
  • 6 Conclusion
  • References
  • Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
  • 1 Introduction
  • 2 Related Work
  • 3 Features Soft-Alignment Graph Neural Networks
  • 4 Experiments
  • 4.1 Experimental Setup
  • 4.2 Ablation Study.
  • 4.3 Graph Classification Results
  • 4.4 Graph Regression Results
  • 5 Conclusion
  • References
  • Review of Handwriting Analysis for Predicting Personality Traits
  • 1 Introduction
  • 1.1 History
  • 1.2 Applications
  • 1.3 Requirements
  • 2 Research Progress
  • 2.1 Advantages
  • 2.2 Disadvantages
  • 3 Research Steps
  • 3.1 Database
  • 3.2 Pre-processing
  • 3.3 Feature Extraction
  • 3.4 Personality Trait
  • 3.5 Prediction Model
  • 3.6 Performance Measurement
  • 4 Experiment and Future Work
  • 4.1 Experiment
  • 4.2 Future Work
  • References.
  • Graph Reduction Neural Networks for Structural Pattern Recognition
  • 1 Introduction and Related Work
  • 2 Graph Matching on GNN Reduced Graphs
  • 2.1 Graph Reduction Neural Network (GReNN)
  • 2.2 Classification of GReNN Reduced Graphs
  • 3 Empirical Evaluations
  • 3.1 Datasets and Experimental Setup
  • 3.2 Analysis of the Structure of the Reduced Graphs
  • 3.3 Classification Results
  • 3.4 Ablation Study
  • 4 Conclusions and Future Work
  • References
  • Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach
  • 1 Introduction
  • 2 Related Work.

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