1. Part I: 얼굴 검출 기법 Part II: 감성 언어 인식 기법 2011. 3. 11( 금 ). 김성호 영남대학교 전자공학과 Brown Bag Seminar

3. Proposed Object Representation Scheme Viewpoint Figure/Ground mask Local appearance For 2D object: (object center, scale) For 3D object: 3D object pose Boundary shape Figure/ground information Appearance codebook Part pose Joint appearance and shape model

5. Mathematical Formulation for Categorization (1/2) Solution: C ategory label, V iewpoint, M ask Key issue: difficult modeling of prior due to complex high dimensions Our approach appearance pose Utilize graphical model especially Directed graphical model (Bayesian Net) V M F A X {C,B} N Top-down Bottom-up Viewpoint Figure-ground Codebook index b2 f4 f5 b4 b5 b6 b3 f3 b1 f1 f2 V M F G {C,B}

6. Learning for Distributed Category Representation CC: Category specific Codebook for top-down inference UC: Universal Codebook for bottom-up inference … … … … … … Joint appearance and boundary with viewpoint Car Airplane Issue How to select optimal codebook (CB) for category representation? Previous constellation model: fixed no. of parts  Cannot handle large variations Why distributed?  To handle large intra class variations

8. Entropy of Candidate Codebook Low entropy  surface marking High entropy  Semantic parts Finding : High entropy codebook in should be selected for surface marking reduction

9. Inference Flow related to Category Model Input … … … Car Airplane … background CB UCB CCB Car category Multi-modal viewpoint Multi-modal figure-ground mask Final result Category Model Part-whole context Part-part context (estimate weight) Dense feature Matching to UC Grouping (similarity & proximity) +

10. Demo of Categorization and Segmentation

12. Examples of Face Detection

13. Test image Bottom-up viewpoints Bottom-up mask Hypothesized viewpoint Hypothesized mask Final Inference result by Boosted MCMC

19. Classifier: Support Vector Machine Feature space Learning : Finding optimal classifier Recognition : Performed by the learned classifier

20. Classifier: Nearest Class Mean Feature space Learning : Finding class means Recognition : Finding nearest class

26. 인식결과 : Nearest Class Mean Classifier 이용 56.7%

27. 인식결과 : SVM 이용 86.6% SVM 인식 기법이 더 우수함 .