Pattern Recognition Applications and Methods 5th International Conference, ICPRAM 2016, Rome, Italy, February 24-26, 2016, Revised Selected Papers

✍ Scribed by Fred, Ana(Editor);Marsico, Maria De(Editor);Baja, Gabriella Sanniti Di(Editor)

• Publisher: Springer International Publishing
• Year: 2017
• Tongue: English
• Leaves: 260
• Series: LNCS 10163;LNCS sublibrary. SL 6, Image processing computer vision pattern recognition and graphics
• Edition: 1st ed. 2017
• Category: Library

✦ Synopsis

This book contains revised and extended versions of selected papers from the 5th International Conference on Pattern Recognition, ICPRAM 2016, held in Rome, Italy, in February 2016.

The 13 full papers were carefully reviewed and selected from 125 initial submissions and describe up-to-date applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance pattern recognition methods.

✦ Table of Contents

Preface......Page 6
Organization......Page 10
Contents......Page 16
1 Introduction......Page 18
2.1 Framework of Representative Graph Kernels......Page 20
2.2 Drawbacks of Existing Graph Kernels......Page 24
3 Graph Kernels Based on the Hadamard Code......Page 25
4.1 Experiments on Artificial Datasets......Page 27
4.2 Experiments on Real-World Graphs......Page 31
4.3 Effect of Assignment of Initial Labels......Page 34
5 Conclusion......Page 35
References......Page 36
1 Introduction......Page 37
2 Game Theory......Page 39
3 Dominant Set Clustering......Page 41
4.1 Document Representation......Page 42
4.3 Graph Construction......Page 43
4.4 Clustering......Page 44
4.6 Clustering Games......Page 45
5 Experimental Setup......Page 46
5.2 Experiments with Feature Selection......Page 48
5.4 Comparison with State-of-the-Art Algorithms......Page 50
5.5 Experiments with No Cluster Number......Page 51
5.6 Experiments on Streaming Data......Page 53
6 Conclusions......Page 56
References......Page 57
1 Introduction......Page 60
2.1 Data Description......Page 61
2.2 Mixture Model......Page 62
3.1 Criterion BIC......Page 64
3.2 Criterion AIC......Page 65
3.3 Criterion NEC......Page 66
4.1 Comparison of the Original and Adapted Versions of the Three Criteria......Page 67
4.2 Influence of the Amount of Side-Information......Page 70
4.3 Iris Data Set......Page 71
4.4 Climatic Data......Page 73
References......Page 75
1 Introduction......Page 77
2 Set Partitioning: Full-Dimensional Case......Page 79
3 Set Packing: Lower-Dimensional Case......Page 87
4 Local Search......Page 90
5.1 Approximations......Page 94
5.2 Equivalent Polynomials......Page 96
5.3 MLE of Partition Functions......Page 98
6 Near-Boolean Games......Page 101
7 Conclusions......Page 102
References......Page 103
1 Introduction......Page 105
2 Gaussian Process Regression......Page 107
3.1 Related Work......Page 108
3.2 Multi-output Joint-Covariance Kernels......Page 109
3.3 GP Regression Using Joint-Covariance......Page 110
4.1 Variational Approximation on Multi-output GP......Page 112
4.2 Distributed Inference on Multi-output GP......Page 113
5 Experiments......Page 114
5.1 Experiments on Theoretical Data......Page 115
5.2 Experiments on Flight Test Data......Page 117
References......Page 119
1.1 Background......Page 121
1.2 Motivation......Page 122
1.3 Data Preparation......Page 123
2 Data Analysis......Page 125
3 Simulation......Page 129
4 Experiment......Page 130
5 Computational Complexity......Page 132
6 Conclusion......Page 134
References......Page 135
1 Introduction......Page 138
3 Related Works......Page 139
3.1 SIFT-128 D Descriptor......Page 140
4 Region Compressed SIFT Descriptor......Page 141
5 Evaluation......Page 142
5.2 Evaluation Measures......Page 143
6.1 UKbench Benchmark......Page 144
6.2 Caltech-Buildings Benchmark......Page 146
6.3 Combination of Image Affine Transformations......Page 147
6.4 Combination of Blurring and Affine Transformation......Page 152
7 Conclusion......Page 153
References......Page 154
1 Introduction......Page 156
2 Related Work......Page 157
3.3 High-Level Reasoning Using Position and Pose Information......Page 160
3.4 Analysis Method......Page 162
3.5 Results and Discussion......Page 163
4.2 Sequence Duration and Frame Skipping......Page 165
4.3 Feature Vectors......Page 166
4.4 Results and Discussion......Page 167
5 Conclusions and Future Work......Page 169
References......Page 170
1 Introduction......Page 173
2 Literature Review......Page 174
3.1 The Site......Page 175
4 Planar Projection......Page 176
4.1 Pre-computation and Post-computation......Page 178
4.2 Error Analysis......Page 179
4.3 Effect of Inaccurate Camera Placement to Traditional Calibration......Page 180
5 Silhouette Tracking......Page 183
6 Swimming Cycle Registration......Page 186
7 Conclusions......Page 189
References......Page 190
1 Introduction......Page 192
2 Bayesian Nonparametrics......Page 194
3 Dirichlet Process......Page 195
3.1 Dirichlet Mixture Model......Page 196
3.2 Gibbs Sampling of Parameters......Page 198
3.3 Gibbs Sampling of Clusters......Page 199
4.1 Bayesian Linear Regression Model......Page 200
4.2 Conjugate Prior for the Bayesian Linear Regression Model......Page 201
4.3 Implementation of Gibbs Sampling of Parameters......Page 202
4.4 Implementation of Gibbs Sampling of Clusters......Page 203
5.1 Clustering Performance......Page 205
5.2 Two Examples......Page 206
6 Conclusions......Page 208
References......Page 209
1 Introduction......Page 211
2 Problem Statement......Page 212
3.2 Recent Works......Page 213
4 Definitions......Page 215
5.1 Number of Bubbles of a 3-D Object......Page 217
5.2 Number of Tunnels of a 3-D Object......Page 219
6.1 Procedure to Compute the Number of Bubbles and Tunnels of a 3-D Object......Page 220
7.1 First Experiment......Page 222
7.4 Fourth Experiment......Page 223
8 Conclusions and Directions for Further Research......Page 225
References......Page 226
1 Introduction......Page 229
2.1 Raindrop Detection in the Daytime......Page 231
2.2 Raindrop Detection at Night......Page 235
3 Experiments......Page 238
4 Conclusion......Page 245
References......Page 246
1 Introduction......Page 247
2.2 Pre-processing Step......Page 250
2.3 Post-processing Steps......Page 251
3 Experiments and Results......Page 252
4 Conclusion......Page 257
References......Page 258
Author Index......Page 260