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Multi-View Super Vector for Action Recognition

Multi-View Super Vector for Action Recognition. Shenzhen Institutes of Advanced Technology, CAS Chinese University of Hong Kong. Limin Wang. Zhuowei Cai. Xiaojiang Peng. Yu Qiao. Content. Motivation M-PCCA model & MVSV representation Experimental Results. Content. Motivation

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Multi-View Super Vector for Action Recognition

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  1. Multi-View Super Vector for Action Recognition Shenzhen Institutes of Advanced Technology, CAS Chinese University of Hong Kong Limin Wang Zhuowei Cai Xiaojiang Peng Yu Qiao

  2. Content • Motivation • M-PCCA model & MVSV representation • Experimental Results

  3. Content • Motivation • M-PCCA model & MVSV representation • Experimental Results

  4. Actions in Video Clips can be captured by ... *Video from chalearn looking at people chanllenge HOG HOF MBHx/MBHy static feature dynamic features

  5. Feature Fusion - Concatenation HOG HOF Concatenation before GMM : HOG + HOF Defect : features presumed to be strongly correlated

  6. Feature Fusion - Kernel Average HOG HOF CodeHOF CodeHOG Concatenation after GMM : CodeHOG + CodeHOF Defect : features presumed to be mutually independent

  7. Decomposition HOG HOF HOG-Specific HOG/HOF-Shared HOF-Specific

  8. HOG-Specific HOG/HOF-Shared HOF-Specific Merit : features are decomposed into relatively independent components

  9. HOG-Specific HOG/HOF-Shared HOF-Specific M-PCCA Mixture of Probabilistic Canonical Correlation Analyzers

  10. Content • Motivation • M-PCCA model & MVSV representation • Experimental Results

  11. Mixture of Probabilistic Models Latent Variable Models V = WZ + Zv Z ~ N(0, I), Zv ~ N(μ, Φ) Probabilistic Principal Component Analysis: Φ = σI. *M. Tipping, C. Bishop Probabilistic Factor Analysis: Φ is diagonal. Probabilistic Canonical Correlation Analyzer.*B. Francis, M. Jordan; K. Arto, S. Kaski X = Wx Z + Zx Y = Wy Z + Zy Z ~ N(0, I), Zx ~ N(μx, Φx),Zy ~ N(μy, Φy). Mixture Version: M-PPCA *M. Tipping, C. Bishop, M-FA *G. Zoubin, G. Hinton

  12. M-PCCA EM Learning Algorithm

  13. M-PCCA

  14. M-PCCA X = Wx Z + Zx Y = Wy Z + Zy

  15. M-PCCA Z2 Z1 Z3 = Shared Part

  16. M-PCCA

  17. M-PCCA gx = Private Part gy

  18. M-PCCA Shared Part + = Multi-View Super Vector gx gy Private Part

  19. Content • Motivation • M-PCCA model & MVSV representation • Experimental Results

  20. MVSV with SVM classifier on HMDB51 with various configurations Performance w.r.t number of Components Performance w.r.t Latent Dimension

  21. Results #components = 256, dimension = 45

  22. Descriptor-level X Fusion GMM SVM Score Y GMM (linear) Kernel-level X Fusion SVM GMM Score Y GMM SVM Score-level X Score Fusion Score GMM SVM Score Y MVSV X M-PCCA SVM Fusion Score Y

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