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Selective visual attention : computational models and applications /

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Visual attention is a relatively new area of study combining a number of disciplines: artificial neural networks, artificial intelligence, vision science and psychology. The aim is to build computational models similar to human vision in order to solve tough problems for many potential applications...

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Bibliographic Details
Main Author: Zhang, Liming, 1943-
Other Authors: Lin, Weisi
Format: eBook
Language:English
Published: Singapore : John Wiley & Sons Singapore Pte. Ltd., 2013.
Subjects:
Computer vision.
Selectivity (Psychology) > Computer simulation.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Preface xi
  • PART I BASIC CONCEPTS AND THEORY 1
  • 1 Introduction to Visual Attention 3
  • 1.1 The Concept of Visual Attention 3
  • 1.1.1 Selective Visual Attention 3
  • 1.1.2 What Areas in a Scene Can Attract Human Attention? 4
  • 1.1.3 Selective Attention in Visual Processing 5
  • 1.2 Types of Selective Visual Attention 7
  • 1.2.1 Pre-attention and Attention 7
  • 1.2.2 Bottom-up Attention and Top-down Attention 8
  • 1.2.3 Parallel and Serial Processing 10
  • 1.2.4 Overt and Covert Attention 11
  • 1.3 Change Blindness and Inhibition of Return 11
  • 1.3.1 Change Blindness 11
  • 1.3.2 Inhibition of Return 12
  • 1.4 Visual Attention Model Development 12
  • 1.4.1 First Phase: Biological Studies 13
  • 1.4.2 Second Phase: Computational Models 15
  • 1.4.3 Third Phase: Visual Attention Applications 17
  • 1.5 Scope of This Book 18
  • References 19
  • 2 Background of Visual Attention
  • Theory and Experiments 25
  • 2.1 Human Visual System (HVS) 25
  • 2.1.1 Information Separation 26
  • 2.1.2 Eye Movement and Involved Brain Regions 28
  • 2.1.3 Visual Attention Processing in the Brain 29
  • 2.2 Feature Integration Theory (FIT) of Visual Attention 29
  • 2.2.1 Feature Integration Hypothesis 30
  • 2.2.2 Confirmation by Visual Search Experiments 31
  • 2.3 Guided Search Theory 39
  • 2.3.1 Experiments: Parallel Process Guides Serial Search 40
  • 2.3.2 Guided Search Model (GS1) 42
  • 2.3.3 Revised Guided Search Model (GS2) 43
  • 2.3.4 Other Modified Versions: (GS3, GS4) 46
  • 2.4 Binding Theory Based on Oscillatory Synchrony 47
  • 2.4.1 Models Based on Oscillatory Synchrony 49
  • 2.4.2 Visual Attention of Neuronal Oscillatory Model 54
  • 2.5 Competition, Normalization and Whitening 56
  • 2.5.1 Competition and Visual Attention 56
  • 2.5.2 Normalization in Primary Visual Cortex 57
  • 2.5.3 Whitening in Retina Processing 59
  • 2.6 Statistical Signal Processing 60
  • 2.6.1 A Signal Detection Approach for Visual Attention 61
  • 2.6.2 Estimation Theory and Visual Attention 62
  • 2.6.3 Information Theory for Visual Attention 63.
  • 4.3.3 Results and Discussion 129
  • 4.4 Phase Spectrum of the Quaternion Fourier Transform Approach 131
  • 4.4.1 Biological Plausibility for Multichannel Representation 131
  • 4.4.2 Quaternion and Its Properties 132
  • 4.4.3 Phase Spectrum of Quaternion Fourier Transform (PQFT) 134
  • 4.4.4 Results Comparison 138
  • 4.4.5 Dynamic Saliency Detection of PQFT 140
  • 4.5 Pulsed Discrete Cosine Transform Approach 141
  • 4.5.1 Approach of Pulsed Principal Components Analysis 141
  • 4.5.2 Approach of the Pulsed Discrete Cosine Transform 143
  • 4.5.3 Multichannel PCT Model 144
  • 4.6 Divisive Normalization Model in the Frequency Domain 145
  • 4.6.1 Equivalent Processes with a Spatial Model in the Frequency Domain 146
  • 4.6.2 FDN Algorithm 149
  • 4.6.3 Patch FDN 150
  • 4.7 Amplitude Spectrum of Quaternion Fourier Transform (AQFT) Approach 152
  • 4.7.1 Saliency Value for Each Image Patch 152
  • 4.7.2 The Amplitude Spectrum for Each Image Patch 153
  • 4.7.3 Differences between Image Patches and their Weighting to Saliency Value 154
  • 4.7.4 Patch Size and Scale for Final Saliency Value 156
  • 4.8 Modelling from a Bit-stream 157
  • 4.8.1 Feature Extraction from a JPEG Bit-stream 157
  • 4.8.2 Saliency Detection in the Compressed Domain 160
  • 4.9 Further Discussions of Frequency Domain Approach 161
  • References 163
  • 5 Computational Models for Top-down Visual Attention 167
  • 5.1 Attention of Population-based Inference 168
  • 5.1.1 Features in Population Codes 170
  • 5.1.2 Initial Conspicuity Values 171
  • 5.1.3 Updating and Transformation of Conspicuity Values 173
  • 5.2 Hierarchical Object Search with Top-down Instructions 175
  • 5.2.1 Perceptual Grouping 175
  • 5.2.2 Grouping-based Salience from Bottom-up Information 176
  • 5.2.3 Top-down Instructions and Integrated Competition 179
  • 5.2.4 Hierarchical Selection from Top-down Instruction 179
  • 5.3 Computational Model under Top-down Influence 180
  • 5.3.1 Bottom-up Low-level Feature Computation 181
  • 5.3.2 Representation of Prior Knowledge 181.
  • 5.3.3 Saliency Map Computation using Object Representation 184
  • 5.3.4 Using Attention for Object Recognition 184
  • 5.3.5 Implementation 185
  • 5.3.6 Optimizing the Selection of Top-down Bias 186
  • 5.4 Attention with Memory of Learning and Amnesic Function 187
  • 5.4.1 Visual Memory: Amnesic IHDR Tree 188
  • 5.4.2 Competition Neural Network Under the Guidance of Amnesic IHDR 191
  • 5.5 Top-down Computation in the Visual Attention System: VOCUS 193
  • 5.5.1 Bottom-up Features and Bottom-up Saliency Map 193
  • 5.5.2 Top-down Weights and Top-down Saliency Map 194
  • 5.5.3 Global Saliency Map 196
  • 5.6 Hybrid Model of Bottom-up Saliency with Top-down Attention Process 196
  • 5.6.1 Computation of the Bottom-up Saliency Map 197
  • 5.6.2 Learning of Fuzzy ART Networks and Top-down Decision 197
  • 5.7 Top-down Modelling in the Bayesian Framework 199
  • 5.7.1 Review of Basic Framework 200
  • 5.7.2 The Estimation of Conditional Probability Density 201
  • 5.8 Summary 202
  • References 202
  • 6 Validation and Evaluation for Visual Attention Models 207
  • 6.1 Simple Man-made Visual Patterns 207
  • 6.2 Human-labelled Images 208
  • 6.3 Eye-tracking Data 209
  • 6.4 Quantitative Evaluation 211
  • 6.4.1 Some Basic Measures 211
  • 6.4.2 ROC Curve and AUC Score 213
  • 6.4.3 Inter-subject ROC Area 213
  • 6.5 Quantifying the Performance of a Saliency Model to Human Eye Movement in Static and Dynamic Scenes 215
  • 6.6 Spearman's Rank Order Correlation with Visual Conspicuity 217
  • References 219
  • PART III APPLICATIONS OF ATTENTION SELECTION MODELS 221
  • 7 Applications in Computer Vision, Image Retrieval and Robotics 223
  • 7.1 Object Detection and Recognition in Computer Vision 224
  • 7.1.1 Basic Concepts 224
  • 7.1.2 Feature Extraction 224
  • 7.1.3 Object Detection and Classification 227
  • 7.2 Attention Based Object Detection and Recognition in a Natural Scene 231
  • 7.2.1 Object Detection Combined with Bottom-up Model 231
  • 7.2.2 Object Detection based on Attention Elicitation 233.
  • 7.2.3 Object Detection with a Training Set 236
  • 7.2.4 Object Recognition Combined with Bottom-up Attention 239
  • 7.3 Object Detection and Recognition in Satellite Imagery 240
  • 7.3.1 Ship Detection based on Visual Attention 242
  • 7.3.2 Airport Detection in a Land Region 245
  • 7.3.3 Saliency and Gist Feature for Target Detection 248
  • 7.4 Image Retrieval via Visual Attention 250
  • 7.4.1 Elements of General Image Retrieval 251
  • 7.4.2 Attention Based Image Retrieval 253
  • 7.5 Applications of Visual Attention in Robots 256
  • 7.5.1 Robot Self-localization 257
  • 7.5.2 Visual SLAM System with Attention 259
  • 7.5.3 Moving Object Detection using Visual Attention 262
  • 7.6 Summary 265
  • References 265
  • 8 Application of Attention Models in Image Processing 271
  • 8.1 Attention-modulated Just Noticeable Difference 271
  • 8.1.1 JND Modelling 272
  • 8.1.2 Modulation via Non-linear Mapping 274
  • 8.1.3 Modulation via Foveation 276
  • 8.2 Use of Visual Attention in Quality Assessment 277
  • 8.2.1 Image/Video Quality Assessment 278
  • 8.2.2 Weighted Quality Assessment by Salient Values 279
  • 8.2.3 Weighting through Attention-modulated JND Map 280
  • 8.2.4 Weighting through Fixation 281
  • 8.2.5 Weighting through Quality Distribution 281
  • 8.3 Applications in Image/Video Coding 282
  • 8.3.1 Image and Video Coding 282
  • 8.3.2 Attention-modulated JND based Coding 284
  • 8.3.3 Visual Attention Map based Coding 285
  • 8.4 Visual Attention for Image Retargeting 287
  • 8.4.1 Literature Review for Image Retargeting 288
  • 8.4.2 Saliency-based Image Retargeting in the Compressed Domain 289
  • 8.5 Application in Compressive Sampling 292
  • 8.5.1 Compressive Sampling 293
  • 8.5.2 Compressive Sampling via Visual Attention 296
  • 8.6 Summary 300
  • References 300
  • PART IV SUMMARY 305
  • 9 Summary, Further Discussions and Conclusions 307
  • 9.1 Summary 308
  • 9.1.1 Research Results from Physiology and Anatomy 308
  • 9.1.2 Research from Psychology and Neuroscience 309
  • 9.1.3 Theory of Statistical Signal Processing 310.
  • 9.1.4 Computational Visual Attention Modelling 310
  • 9.1.5 Applications of Visual Attention Models 313
  • 9.2 Further Discussions 314
  • 9.2.1 Interaction between Top-down Control and Bottom-up Processing in Visual Search 314
  • 9.2.2 How to Deploy Visual Attention in the Brain? 315
  • 9.2.3 Role of Memory in Visual Attention 316
  • 9.2.4 Mechanism of Visual Attention in the Brain 316
  • 9.2.5 Covert Visual Attention 317
  • 9.2.6 Saliency of Large Smooth Objects 317
  • 9.2.7 Invariable Feature Extraction 320
  • 9.2.8 Role of Visual Attention Models in Applications 320
  • 9.3 Conclusions 320
  • References 321
  • Index 325.