Visual Object Recognition (Synthesis Lectures on Artificial Intelligence and Machine Learning)

✍ Scribed by Kristen Grauman, Bastian Leibe

• Publisher: MC
• Year: 2011
• Tongue: English
• Leaves: 183
• Category: Library

✦ Table of Contents

Preface......Page 13
Acknowledgments......Page 15
Figure Credits......Page 17
Overview......Page 21
Challenges......Page 23
The State of the Art......Page 24
Global Image Representations......Page 27
Local Feature Representations......Page 29
Introduction......Page 31
Keypoint Localization......Page 32
Scale Invariant Region Detection......Page 35
Affine Covariant Region Detection......Page 40
Orientation Normalization......Page 41
The SIFT Descriptor......Page 42
The SURF Detector/Descriptor......Page 44
Concluding Remarks......Page 45
Matching Local Features......Page 47
Tree-based Algorithms......Page 48
Hashing-based Algorithms and Binary Codes......Page 51
Indexing Features with Visual Vocabularies......Page 54
Creating a Visual Vocabulary......Page 56
Choices in Vocabulary Formation......Page 57
Inverted File Indexing......Page 58
Concluding Remarks......Page 60
Estimating Geometric Models......Page 63
Estimating Affine Transformations......Page 64
Homography Estimation......Page 65
More General Transformations......Page 67
RANSAC......Page 68
Generalized Hough Transform......Page 71
Discussion......Page 72
Object Recognition......Page 75
Large-Scale Image Retrieval......Page 78
Concluding Remarks......Page 79
Overview: Recognition of Generic Object Categories......Page 81
Pixel Intensities and Colors......Page 83
Window Descriptors: Global Gradients and Texture......Page 84
Patch Descriptors: Local Gradients and Texture......Page 85
A Hybrid Representation: Bags of Visual Words......Page 88
Feature Selection......Page 89
Part-based Object Representations......Page 90
Overview of Part-Based Models......Page 91
Fully-Connected Models: The Constellation Model......Page 93
Star Graph Models......Page 94
Mixed Representations......Page 96
Concluding Remarks......Page 97
Detection via Classification......Page 99
Speeding up Window-based Detection......Page 100
Limitations of Window-based Detection......Page 101
Voting and the Generalized Hough Transform......Page 103
Generalized Distance Transform......Page 105
Data Annotation......Page 107
Learning Window-based Models......Page 109
Specialized Similarity Measures and Kernels......Page 110
Learning in the Constellation Model......Page 119
Learning in the Implicit Shape Model......Page 120
Learning in the Pictorial Structure Model......Page 121
Training Process......Page 123
Discussion......Page 125
The HOG Person Detector......Page 127
Training Process......Page 128
Discussion......Page 129
Training Process......Page 130
Vote Backprojection and Top-Down Segmentation......Page 131
Discussion......Page 133
Recognition Process......Page 135
Discussion......Page 138
Benchmarks and Datasets......Page 139
Context-based Recognition......Page 142
Multi-Viewpoint and Multi-Aspect Recognition......Page 143
Integrated Segmentation and Recognition......Page 144
Using Weakly Labeled Image Data......Page 146
Unsupervised Object Discovery......Page 147
Language, Text, and Images......Page 148
Conclusions......Page 151
Bibliography......Page 153
Authors' Biographies......Page 183