1.8 KiB
1.8 KiB
CSE559A Lecture 20
Local feature descriptors
Detection: Identify the interest points
Description: Extract vector feature descriptor surrounding each interest point.
Matching: Determine correspondence between descriptors in two views
Image representation
Histogram of oriented gradients (HOG)
- Quantization
- Grids: fast but applicable only with few dimensions
- Clustering: slower but can quantize data in higher dimensions
- Matching
- Histogram intersection or Euclidean may be faster
- Chi-squared often works better
- Earth mover’s distance is good for when nearby bins represent similar values
SIFT vector formation
Computed on rotated and scaled version of window according to computed orientation & scale
- resample the window
Based on gradients weighted by a Gaussian of variance half the window (for smooth falloff)
4x4 array of gradient orientation histogram weighted by magnitude
8 orientations x 4x4 array = 128 dimensions
Motivation: some sensitivity to spatial layout, but not too much.
For matching:
- Extraordinarily robust detection and description technique
- Can handle changes in viewpoint
- Up to about 60 degree out-of-plane rotation
- Can handle significant changes in illumination
- Sometimes even day vs. night
- Fast and efficient—can run in real time
- Lots of code available
SURF
- Fast approximation of SIFT idea
- Efficient computation by 2D box filters & integral images
- 6 times faster than SIFT
- Equivalent quality for object identification
Shape context
Self-similarity Descriptor
