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Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17–19, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Xiao Bai

    1. Beihang University, Beijing, China

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  2. Edwin R. Hancock

    1. University of York, York, United Kingdom

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  3. Tin Kam Ho

    1. IBM Research – Thomas J. Watson Research, Yorktown Heights, USA

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  4. Richard C. Wilson

    1. University of York, Heslington, York, United Kingdom

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  5. Battista Biggio

    1. University of Cagliari, Cagliari, Italy

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  6. Antonio Robles-Kelly

    1. Data 61 - CSIRO, Canberra, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11004)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (49 papers)

  1. Front Matter

    Pages I-XIII
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  2. Classification and Clustering

    1. Front Matter

      Pages 1-1
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    2. Image Annotation Using a Semantic Hierarchy

      • Abdessalem Bouzaieni, Salvatore Tabbone
      Pages 3-13

    3. Malignant Brain Tumor Classification Using the Random Forest Method

      • Lichi Zhang, Han Zhang, Islem Rekik, Yaozong Gao, Qian Wang, Dinggang Shen
      Pages 14-21

    4. Rotationally Invariant Bark Recognition

      • Václav Remeš, Michal Haindl
      Pages 22-31

    5. Dynamic Voting in Multi-view Learning for Radiomics Applications

      • Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
      Pages 32-41

    6. Iterative Deep Subspace Clustering

      • Lei Zhou, Shuai Wang, Xiao Bai, Jun Zhou, Edwin Hancock
      Pages 42-51

    7. A Scalable Spectral Clustering Algorithm Based on Landmark-Embedding and Cosine Similarity

      • Guangliang Chen
      Pages 52-62

  3. Deep Learning and Neural Networks

    1. Front Matter

      Pages 63-63
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    2. On Fast Sample Preselection for Speeding up Convolutional Neural Network Training

      • Frédéric Rayar, Seiichi Uchida
      Pages 65-75

    3. UAV First View Landmark Localization via Deep Reinforcement Learning

      • Xinran Wang, Peng Ren, Leijian Yu, Lirong Han, Xiaogang Deng
      Pages 76-85

    4. Context Free Band Reduction Using a Convolutional Neural Network

      • Ran Wei, Antonio Robles-Kelly, José Álvarez
      Pages 86-96

    5. Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks

      • Évariste Daller, Sébastien Bougleux, Luc Brun, Olivier Lézoray
      Pages 97-106

    6. Learning Deep Embeddings via Margin-Based Discriminate Loss

      • Peng Sun, Wenzhong Tang, Xiao Bai
      Pages 107-115

  4. Dissimilarity Representations and Gaussian Processes

    1. Front Matter

      Pages 117-117
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    2. Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning

      • Antonelli Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax
      Pages 119-129

    3. Local Binary Patterns Based on Subspace Representation of Image Patch for Face Recognition

      • Xin Zong
      Pages 130-139

    4. An Image-Based Representation for Graph Classification

      • Frédéric Rayar, Seiichi Uchida
      Pages 140-149

    5. Visual Tracking via Patch-Based Absorbing Markov Chain

      • Ziwei Xiong, Nan Zhao, Chenglong Li, Jin Tang
      Pages 150-159

    6. Gradient Descent for Gaussian Processes Variance Reduction

      • Lorenzo Bottarelli, Marco Loog
      Pages 160-169
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  1. Structural, Syntactic, and Statistical Pattern Recognition

Keywords

About this book

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. 

Editors and Affiliations

  • Beihang University, Beijing, China

    Xiao Bai

  • University of York, York, United Kingdom

    Edwin R. Hancock

  • IBM Research – Thomas J. Watson Research, Yorktown Heights, USA

    Tin Kam Ho

  • University of York, Heslington, York, United Kingdom

    Richard C. Wilson

  • University of Cagliari, Cagliari, Italy

    Battista Biggio

  • Data 61 - CSIRO, Canberra, Australia

    Antonio Robles-Kelly

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