LEADER 12726nam 22008535 450 001 9910639901403321 005 20251225203657.0 010 $a3-031-23028-0 024 7 $a10.1007/978-3-031-23028-8 035 $a(MiAaPQ)EBC7166020 035 $a(Au-PeEL)EBL7166020 035 $a(CKB)25913967000041 035 $a(PPN)267813198 035 $a(BIP)87286906 035 $a(BIP)86353645 035 $a(DE-He213)978-3-031-23028-8 035 $a(EXLCZ)9925913967000041 100 $a20221224d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStructural, Syntactic, and Statistical Pattern Recognition $eJoint IAPR International Workshops, S+SSPR 2022, Montreal, QC, Canada, August 26?27, 2022, Proceedings /$fedited by Adam Krzyzak, Ching Y. Suen, Andrea Torsello, Nicola Nobile 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (336 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13813 311 08$aPrint version: Krzyzak, Adam Structural, Syntactic, and Statistical Pattern Recognition Cham : Springer International Publishing AG,c2023 9783031230271 320 $aIncludes bibliographical references and index. 327 $aIntro -- 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. 327 $aReview 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 -- 3 Hybrid Generative-Discriminative Approach with Fisher Kernels -- 3.1 Hidden Markov Models -- 3.2 Inference on Hidden States: Forward-Backward Algorithm -- 3.3 Fisher Kernels -- 4 Experiments -- 4.1 Problem Modeling -- 4.2 Datasets -- 4.3 Results -- 5 Conclusion -- References -- Spatio-Temporal United Memory for Video Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Dual-Flow Structure Based on Autoencoder -- 2.2 Memory -- 3 Methodology -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Comparison with Existing Methods -- 4.3 Ablation Experiments -- 4.4 Running Time -- 5 Conclusion -- References -- A New Preprocessing Method for Measuring Image Visual Quality Robust to Rotation and Spatial Shifts -- 1 Introduction -- 2 Proposed Preprocessing Method -- 3 Experimental Results -- 4 Conclusions. 327 $aReferences -- Learning Distances Between Graph Nodes and Edges -- 1 Introduction -- 2 Related Work -- 2.1 Graph Edit Distance -- 2.2 Learning the Edit Costs -- 3 Method -- 3.1 The Learning Method -- 3.2 The Algorithm -- 4 Practical Experiments -- 5 Conclusions -- References -- Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN -- 1 Introduction -- 2 Out-of-Distribution Detection with DLSGAN -- 3 Experiments -- 3.1 Experiments Settings -- 3.2 Experiments Results -- 4 Conclusion -- References -- A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Spectral Signatures: HKS and WKS -- 3.2 Wasserstein Distance -- 4 From Spectral Signatures to Graph Kernels -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Sensitivity Study -- 5.3 Graph Classification Results -- 6 Conclusion -- References -- Discovering Respects for Visual Similarity -- 1 Introduction -- 2 Method -- 2.1 Dataset Selection -- 2.2 Image Representation -- 2.3 What -- 2.4 Why -- 3 Evaluations -- 3.1 Automatic Interpretability and Human Validation -- 3.2 Human Assessment of Cluster Quality -- 4 Conclusion -- References -- Graph Regression Based on Graph Autoencoders -- 1 Introduction -- 2 Related Work -- 2.1 Graph Embedding and Graph Regression -- 2.2 Autoencoders and Graph Autoencoders -- 2.3 Prediction of Chemical Compound Properties -- 3 The Method -- 4 Motive and Practical Application -- 4.1 Database -- 4.2 Architecture Configuration -- 4.3 Energy Prediction -- 4.4 Runtime Analysis -- 5 Conclusions and Future Work -- References -- Distributed Decision Trees -- 1 Introduction -- 2 Different Tree Architectures -- 2.1 Hard Decision Trees -- 2.2 Soft Decision Trees -- 2.3 Budding Trees -- 3 Distributed Budding Trees -- 4 Experiments -- 5 Visualization -- 6 Conclusions -- References. 327 $aA Capsule Network for Hierarchical Multi-label Image Classification -- 1 Introduction -- 2 Hierarchical Multi-label Capsules -- 3 Experiments -- 3.1 Implementation Details and Datasets -- 3.2 Experimental Setup -- 3.3 Results -- 4 Conclusions -- References -- Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification -- 1 Introduction -- 2 Literature Review -- 2.1 ECG Classification -- 2.2 Uncertainty Estimation in Medical Image Analysis -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 CNN Architecture -- 3.4 Monte Carlo Dropout -- 4 Experimental Results -- 4.1 Experimentation -- 4.2 Result Analysis and Discussion -- 5 Conclusion and Future Work -- References -- One-Against-All Halfplane Dichotomies -- 1 Introduction -- 2 Prior Work -- 3 One-Against-All Halfplane Dichotomies -- 4 A Geometric Perspective -- 5 Pairwise Attribute Difference Vectors -- 6 Linear Programming and Neural Networks -- 7 Ranking -- 8 Summary -- References -- Fast Distance Transforms in Graphs and in Gmaps -- 1 Introduction -- 1.1 Notations and Definitions -- 2 Distance Transform in a Graph -- 2.1 Geodesic Distance Transform -- 3 Distance Transforms in n-Gmaps -- 4 Results -- 5 Conclusions -- References -- Retargeted Regression Methods for Multi-label Learning -- 1 Introduction -- 2 Proposal: Retargeted Multi-label Least Square Regression -- 2.1 Notations -- 2.2 Brief Review of ReLSR -- 2.3 Problem Formulation -- 2.4 Optimization -- 2.5 Computational Complexity -- 2.6 Learning Threshold -- 3 Experiments -- 3.1 Dataset and Evaluation Measurement -- 3.2 Settings -- 3.3 Results -- 4 Conclusion -- References -- Transformer with Spatio-Temporal Representation for Video Anomaly Detection -- 1 Introduction -- 2 Related Work -- 2.1 Video Anomaly Detection -- 2.2 Transformer -- 3 Methodology -- 4 Experiments. 327 $a4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparison with Existing State-of-the-Arts -- 4.4 Ablation Experiments -- 4.5 Running Time -- 5 Conclusion -- References -- Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings -- 1 Introduction -- 2 Kernelized Implicit Mapping -- 3 LOO Evaluation of KIM -- 4 Positioning of this Study -- 5 Kernels -- 6 Multiple Applications of LOO Matrix -- 6.1 Visualization and Model Selection -- 6.2 Nonlinear Classification -- 6.3 Recovery -- 7 Analysis of LOO Matrix -- 8 Discussion -- 9 Conclusion -- References -- Graph Similarity Using Tree Edit Distance -- 1 Introduction -- 2 Motivation and Basic Concepts -- 3 Algorithm -- 4 Experimental Results -- 5 Conclusion -- References -- Data Augmentation on Graphs for Table Type Classification -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing -- 3.2 Data Augmentation -- 3.3 Model -- 4 Experiments -- 4.1 The Tab2Know Dataset -- 4.2 Using the Dataset -- 4.3 Results -- 5 Conclusions -- References -- Improved Training for 3D Point Cloud Classification -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preliminaries -- 3.2 Proposed Method -- 3.3 Proposed Training Protocol -- 4 Experimental Results -- 4.1 Hyper-parameter Sensitivity -- 4.2 Results for Proposed Model Variants -- 4.3 Results for Explored Loss Functions -- 4.4 Explored Training Protocol -- 4.5 Effect of Augmentation -- 4.6 Transfer Learning -- 4.7 Confusion Matrix -- 5 Conclusions -- References -- On the Importance of Temporal Features in Domain Adaptation Methods for Action Recognition -- 1 Introduction -- 2 Related Works -- 3 Recalls Basics of an Architecture for Domain Adaptation -- 4 The New Designed Architecture -- 5 Experiments and Results -- 5.1 Datasets and Metrics -- 5.2 Parameters Setting Details -- 5.3 Results and Comments -- 6 Conclusions. 327 $aReferences. 330 $aThis book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13813 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aDiscrete mathematics 606 $aAlgorithms 606 $aComputer graphics 606 $aComputer vision 606 $aPattern recognition systems 606 $aArtificial Intelligence 606 $aDiscrete Mathematics in Computer Science 606 $aDesign and Analysis of Algorithms 606 $aComputer Graphics 606 $aComputer Vision 606 $aAutomated Pattern Recognition 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 0$aDiscrete mathematics. 615 0$aAlgorithms. 615 0$aComputer graphics. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 14$aArtificial Intelligence. 615 24$aDiscrete Mathematics in Computer Science. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputer Graphics. 615 24$aComputer Vision. 615 24$aAutomated Pattern Recognition. 676 $a006.37 676 $a006.4 702 $aKrzyzak$b Adam 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910639901403321 996 $aStructural, Syntactic, and Statistical Pattern Recognition$9772717 997 $aUNINA