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Descriptors ( Description of Interest Regions with Local Binary Patterns)

Descriptors ( Description of Interest Regions with Local Binary Patterns). Yu-Lin Cheng (03/07/2011). Outline. Scale Invariant Feature Transform (SIFT) Descriptor Local Binary Pattern (LBP ) Descriptor Center-Symmetric LBP (CS-LBP) Descriptor

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Descriptors ( Description of Interest Regions with Local Binary Patterns)

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  1. Descriptors(Description of Interest Regions with Local Binary Patterns) Yu-Lin Cheng (03/07/2011)

  2. Outline • Scale Invariant Feature Transform (SIFT) Descriptor • Local Binary Pattern (LBP) Descriptor • Center-Symmetric LBP (CS-LBP) Descriptor • Histogram of Oriented Gradients (HOG) Descriptor

  3. descriptor SIFT(Scale Invariant Feature Transform ) • SIFT Algorithm:

  4. Good ! bad scale scale SIFT(Scale Invariant Feature Transform ) • Scale-space Extrema Detection: • Stable feature points ----- (scale invariant) • Principle: • A local maximum over scales by using combination of normalized derivatives can be treated as a characteristic point of local structure • Use LoG to find maximum

  5. SIFT(Scale Invariant Feature Transform ) • Scale-space Extrema Detection: • Use DoG instead of LoG ---- (computational efficiency)

  6. SIFT(Scale Invariant Feature Transform ) • Scale-space Extrema Detection:

  7. SIFT(Scale Invariant Feature Transform ) • Scale-space Extrema Detection: • Local extrema detection: • Compare to 26 neighbors • Keep the same keypoint in all scale

  8. SIFT(Scale Invariant Feature Transform ) • Scale-space Extrema Detection: • Reject points with low contrast

  9. SIFT(Scale Invariant Feature Transform ) • Accurate keypoints localization: • Quadratic function to interpolate the location of maximum • Eliminate edge response: r: threshold, H: Hessian matrix

  10. SIFT(Scale Invariant Feature Transform ) • Orientation Assignment: • Assign a consistent orientation to achieve orientation invariant • Method:

  11. SIFT(Scale Invariant Feature Transform ) • Orientation Assignment: • Calculate gradient magnitude and direction of neighboring pixels

  12. SIFT(Scale Invariant Feature Transform ) • Orientation Assignment: • Calculate weighted orientation histogram

  13. SIFT(Scale Invariant Feature Transform ) • Orientation Assignment: • Calculate weighted orientation histogram

  14. SIFT(Scale Invariant Feature Transform ) • Orientation Assignment: • Calculate weighted orientation histogram

  15. SIFT(Scale Invariant Feature Transform ) • Keypoints Descriptor: • Empirical result: • Cell size: 44 pixels • Block size: 44 cells • Dimension: 44 (cells) 8 (bins) = 128 Weighted magnitude

  16. SIFT(Scale Invariant Feature Transform ) • Keypoints Descriptor: • Avoid all boundary effect • Use trilinear interpolation • Normalization: (illumination invariant) • Normalize to unit length • Threshlod the maximum value to 0.2 • Match the magnitudes for large gradients is no longer important • Renormalize to unit length

  17. LBP(Local Binary Pattern) • A powerful mean of texture description • LBP operator: • Standard LBP: • Illustration:

  18. LBP(Local Binary Pattern) • Example: • Parameters: • P : Number of neighboring pixels • R : Radius

  19. LTP(Local Trinary Pattern) • LTP operator: • t : threshold • Illustration:

  20. CS-LBP(Center-Symmetric Local Binary Pattern) • CS-LBP operator: • Illustration:

  21. CS-LBP Descriptor • Flow diagram:

  22. CS-LBP Descriptor • Interest Region Detection: • Detectors: • 1. Hessian-Affine (blob-like structure) • 2. Harris-Affine(corner-like structure) • 3. Hessian-Laplace(scale-invariant version) • 4. Harris-Laplace (scale-invariant version) 4141

  23. CS-LBP Descriptor • Feature Extraction: • CS-LBP operator: • Parameters: • R: radius • R = 1, 2 • N: number of neighboring pixels • N = 6, 8 • T: threshold • T = 0.2 • Descriptor Construction: • Location grids • 33 cells/44 cells • Avoid boundary effects: • Using ‘bilinear interpolation’ 4141

  24. CS-LBP Descriptor • Descriptor Normalization: (illumination invariant) • Normalize to unit length • Thresholding • Renormalize to unit length

  25. Comparison(SIFT v.s. CS-LBP) • Assumption: • Computations cannot be reused from detection algorithm • Comparison: • Conclusion: • Computational efficiency and better performance than SIFT

  26. HOG(Histogram of Oriented Gradients)

  27. HOG(Histogram of Oriented Gradients) • Gradient Computation:

  28. HOG(Histogram of Oriented Gradients) • Gradient Computation:

  29. HOG(Histogram of Oriented Gradients) • Spatial/Orientation Binning: • Weighted votes • Function of magnitude • Avoid aliasing • Interpolation • Parameters: • Number of orientation bins • Cell size • Block size Cell Block

  30. HOG(Histogram of Oriented Gradients) • Spatial/Orientation Binning: • Parameters: • Number of orientation bins: 9 bins/18bins • Cell size: 88 pixels • Block size: 22 cells

  31. HOG(Histogram of Oriented Gradients) • Normalization: • Group cells to larger blocks and normalize each block separately(illumination invariant) • Normalization Schemes:

  32. HOG(Histogram of Oriented Gradients) • Normalization: • Normalization Schemes:

  33. Comparison(SIFT v.s. HOG) • Comparison:

  34. HOG Variation • ‘Object Detection with Discriminatively Trained Part Based Models’ • Pixel-Level Feature Maps: • Use [-1, 0, 1] to calculate gradient • Contrast sensitive(B1), Contrast insensitive(B2) ,(p = 9) • Quantize into orientation bins r: gradient magnitude

  35. HOG Variation • Spatial Aggregation: • Rectangular cell: 88 pixels • Cell-based feature map: • Reduce the size of feature map • Avoid aliasing: • Bilinear interpolation • Normalization:

  36. HOG Variation • Truncation: maximum 0.2 • No renormalization • Dimension: • 9 bins 4 different normalization = 36 (contrast insensitive)

  37. HOG Variation • PCA analysis: • Top 11 eigenvectors captures most of information of HOG

  38. HOG Variation • PCA analysis: • Top eigenvectors lie (approximately) in a linear subspace • 13-dimensional features: • Project 36-dimensional HOG feature into uk, vk • Projection into uk: sum over 4 normalization over fixed orientation • Projection into vk: sum over 9 orientation over fixed normalization

  39. HOG Variation • For Contrast Insensitive(B2): • 9 bins 4 different normalization = 36 (contrast insensitive) • For Contrast Sensitive(B1): • 18 bins 4 different normalization = 72 (contrast insensitive) • Reduce to (18 + 9) + 4 = 31 dimension

  40. Reference • “Description of Interest Regions With Local Binary Patterns”, Pattern Regonization ’09 Marko Heikkilä • http://www.tele.ucl.ac.be/~devlees/ref_ELEC2885/projects/RoIdescriptionLBP-pr-accepted.pdf • “Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns”, YoungbinZheng • “Scale-space Theory” Tony Lindeberg • “Histogram of Oriented Gradients for Human Detection”, CVPR ‘05 NavneetDalal • “Finding People in Images and Videos”, NavneetDalal • “Feature matching” Yung-Yu Chuang • “Scale & Affine Invariant Interest Point Detectors”, IJCV ’04 KrystianMikolajczyk

  41. Reference • “Object Detection with Discriminatively Trained Part Based Models” • “Distinctive Image Features from Scale-Invariant Keypoints”, IJCV ’04 David G. Lowe • http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.157.3843&rep=rep1&type=pdf

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