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Classifying Covert Photographs

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.

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Classifying Covert Photographs

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  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. Introduction • Challenges • Database construction • Training set covert:1200 regular:4800 • Testing set covert:300 regular:1200 • Attribute annotation

  6. Combine Image Features and Attributes

  7. Combine Image Features and Attributes • 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

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