1. Classifying Covert Photographs CVPR 2012 POSTER

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

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

4. Introduction

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

6. Combine Image Features and Attributes

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

8. 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

9. Combine Image Features and Attributes  Attribute Classifiers and Attribute Features

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

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

12. Experiment  Performance evaluation metrics  AUC  1-EER

13. Experiment  Evaluation of MKL algorithm

14. Experiment  Evaluation of MKL algorithm

15. Experiment  Evaluation of MKL algorithm

16. Experiment  Evaluation of MKL algorithm

17. Experiment  Evaluation of MKL algorithm

18. Experiment  Evaluation of MKL algorithm

19. Experiment

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