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+# 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
+
+![Shape context descriptor](https://notenextra.trance-0.com/CSE559A/Shape_context_descriptor.png)
+
+#### Self-similarity Descriptor
+
+![Self-similarity descriptor](https://notenextra.trance-0.com/CSE559A/Self-similarity_descriptor.png)
+
+## Local feature matching
+
+### Matching