Classifying Covert Photographs CVPR 2012 POSTER

Outline  Introduction  Combine Image Features and Attributes  Experiment  Conclusion

Introduction  Why doing this classification?  Image/video acquisition devices  New Internet technologies  What is covert?  Secret photography

Introduction

 Challenges  Database construction  Training set covert:1200 regular:4800  Testing set covert:300 regular:1200  Attribute annotation

Combine Image Features and Attributes

 Low-Level Image Features  Bag of Features(BoF)  Color GIST  Color moments  Edge Orientation Histogram  Gray Histogram

Combine Image Features and Attributes  Low-Level Image Features  Gray Level Co-occurrence Matrix  Hue descriptor  Local Binary Pattern  Pyramid histogram of orientation gradient  Spatiogram

Combine Image Features and Attributes  Attribute Classifiers and Attribute Features

Combine Image Features and Attributes  Fusion with Multiple Kernels Learning(MKL)

Combine Image Features and Attributes  Fusion with Multiple Kernels Learning(MKL)  Feature normalization and kernel standardization

Experiment  Performance evaluation metrics  AUC  1-EER

Experiment  Evaluation of MKL algorithm

Experiment  Evaluation of MKL algorithm

Experiment  Evaluation of MKL algorithm

Experiment  Evaluation of MKL algorithm

Experiment  Evaluation of MKL algorithm

Experiment  Evaluation of MKL algorithm

Experiment

Conclusion  Appropriate features are really important to the accuracy.  Multiple Kernel Learning