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SIFT: Scale-Invariant Feature Transform for Interest Point Descriptors

Learn about the SIFT algorithm for eliminating rotational ambiguity, computing appearance descriptors, and extracting affine regions in computer vision.

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SIFT: Scale-Invariant Feature Transform for Interest Point Descriptors

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  1. Interest Point Descriptors CS491Y/691Y Topics in Computer Vision Dr. George Bebis

  2. Eliminate rotational ambiguity Compute appearancedescriptors Extract affine regions Normalize regions SIFT (Lowe ’04) Interest Point Descriptors Descriptors

  3. Scale Invariant Feature Transform (SIFT) • Take a 16 x16 window around interest point (i.e., at the scale detected). • Divide into a 4x4 grid of cells. • Compute histogram of image gradient directions in each cell (8 bins each). 16 histograms x 8 orientations = 128 features

  4. Properties of SIFT • Highly distinctive • A single feature can be correctly matched with high probability against a large database of features from many images. • Scale and rotation invariant. • Partially invariant to 3D camera viewpoint • Can tolerate up to about 60 degree out of plane rotation • Partially invariant to changes in illumination • Can be computed fast and efficiently.

  5. SIFT Computation – Steps (1) Scale-space extrema detection • Extract scale and rotation invariant interest points (i.e., keypoints). (2) Keypoint localization • Determine location and scale for each interest point. • Eliminate “weak” keypoints (3) Orientation assignment • Assign one or more orientations to each keypoint. (4) Keypoint descriptor • Use local image gradients at the selected scale. D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2):91-110, 2004.

  6. Scale-space Extrema Detection Harris-Laplace Find local maxima of: Harris detector in space LoG in scale scale  LoG  y x  Harris  scale • SIFT • Find local maxima of: • Hessian in space • DoG in scale  DoG  y x  Hessian 

  7. 1. Scale-space Extrema Detection (cont’d) • DoG images are grouped by octaves (i.e., doubling of σ0) • Fixed number of levels per octave 22σ0 down-sample where 2σ0 σ0

  8. 1. Scale-space Extrema Detection (cont’d) ksσ0 (ks=2) • Images within each octave are • separated by a constant factor k • If each octave is divided in s-1 intervals: • ks=2 or k=21/s … k2σ0 k1σ0 k0σ0

  9. SIFT parameters • Parameters (i.e., scales per octave, σ0 etc.) were chosen experimentally based on keypoint (i) repeatability, (ii) localization, and (iii) matching accuracy. • From Lowe’s paper: • Number of scales per octave: 3 • σ0 =1.6

  10. 1. Scale-space Extrema Detection (cont’d) • Extract local extrema (i.e., minima or maxima) in DoG pyramid. • Compare each point to its 8 neighbors at the same level, 9 neighbors • in the level above, and 9 neighbors in the level below (i.e., 26 total).

  11. 2. Keypoint Localization Determine the location and scale of keypoints to sub-pixel and sub-scale accuracy by fitting a 3D quadratic polynomial: keypoint location offset sub-pixel, sub-scale estimated location Substantial improvement to matching and stability!

  12. If reject keypoint • i.e., assumes that image values have been normalized in [0,1] 2. Keypoint Localization (cont’d) • Rejectkeypoints having low contrast. • i.e., sensitive to noise

  13. 2. Keypoint Localization (cont’d) • Reject points lying on edges (or being close to edges) • Harris uses the auto-correlation matrix: R(AW) = det(AW) – α trace2(AW) or R(AW) = λ1λ2- α (λ1+ λ2)2

  14. 2. Keypoint Localization (cont’d) • SIFT uses the Hessian matrix. • i.e., Hessian encodes principal curvatures α: largest eigenvalue (λmax) β: smallest eigenvalue (λmin) (proportional to principal curvatures) (r = α/β) (SIFT uses r = 10) Reject keypoint if:

  15. 2. Keypoint Localization (cont’d) • (a) 233x189 image • (b) 832 DoGextrema • (c) 729 left after low • contrast threshold • (d) 536 left after testing • ratio based on Hessian

  16. p 2 0 3. Orientation Assignment • Create histogram of gradient directions, within a region around the keypoint, at selected scale: 36 bins (i.e., 10o per bin) • Histogram entries areweighted by (i) gradient magnitude and (ii) a Gaussian function with σ equal to 1.5 times the scale of the keypoint.

  17. p 2 0 3. Orientation Assignment (cont’d) • Assign canonical orientation at peak of smoothed histogram (fit parabola to better localize peak). • In case of peaks within 80% of highest peak, multiple orientations assigned to keypoints. • About 15% of keypoints has multiple orientations assigned. • Significantly improves stability of matching.

  18. 4. Keypoint Descriptor • Take a 16 x16 window around detected interest point. • Divide into a 4x4 grid of cells. • Compute histogram in each cell. (8 bins) 16 histograms x 8 orientations = 128 features

  19. 4. Keypoint Descriptor (cont’d) • Each histogram entry isweighted by (i) gradient magnitude and (ii) a Gaussian function with σ equal to 0.5 times the width of the descriptor window.

  20. 4. Keypoint Descriptor (cont’d) Partial Voting: distribute histogram entries into adjacent bins (i.e., additional robustness to shifts) Each entry is added to all bins, multiplied by a weight of 1-d, where d is the distance from the bin it belongs.

  21. 4. Keypoint Descriptor (cont’d) • Descriptor depends on two main parameters: • (1) number of orientations r • (2) n x n array of orientation histograms rn2 features SIFT: r=8, n=4 128 features

  22. 4. Keypoint Descriptor (cont’d) • Invariance to linear illumination changes: • Normalization to unit length is sufficient. 128 features

  23. 4. Keypoint Descriptor (cont’d) • Non-linear illumination changes: • Saturation affects gradient magnitudes more than orientations • Threshold entries to be no larger than 0.2 and renormalize to unit length 128 features

  24. Matching SIFT features I2 I1 • Given a feature in I1, how to find the best match in I2? • Define distance function that compares two descriptors. • Test all the features in I2, find the one with min distance.

  25. Matching SIFT features (cont’d) f1 f2 I1 I2

  26. Matching SIFT features (cont’d) • Accept a match if SSD(f1,f2) < t • How do we choose t?

  27. Matching SIFT features (cont’d) f1 f2' f2 I1 I2 • A better distance measure is the following: • SSD(f1, f2) / SSD(f1, f2’) • f2 is best SSD match to f1 in I2 • f2’ is 2nd best SSD match to f1 in I2

  28. Matching SIFT features (cont’d) • Accept a match if SSD(f1, f2) / SSD(f1, f2’) < t • t=0.8 has given good results in object recognition. • 90% of false matches were eliminated. • Less than 5% of correct matches were discarded

  29. Matching SIFT features (cont’d) How to evaluate the performance of a feature matcher? 50 75 200

  30. Matching SIFT features (cont’d) True positives (TP) = # of detected matches that are correct False positives (FP) = # of detected matches that are incorrect 50 true match 75 200 false match • Threshold t affects # of correct/false matches

  31. Matching SIFT features(cont’d) 0.7 TPrate 0 1 FP rate 0.1 • ROC Curve • - Generated by computing (FP, TP) for different thresholds. • - Maximize area under the curve (AUC). 1 http://en.wikipedia.org/wiki/Receiver_operating_characteristic

  32. Applications of SIFT Object recognition Object categorization Location recognition Robot localization Image retrieval Image panoramas

  33. Variations of SIFT features • PCA-SIFT • SURF • GLOH

  34. SIFT Steps - Review (1) Scale-space extrema detection • Extract scale and rotation invariant interest points (i.e., keypoints). (2) Keypoint localization • Determine location and scale for each interest point. • Eliminate “weak” keypoints (3) Orientation assignment • Assign one or more orientations to each keypoint. (4) Keypoint descriptor • Use local image gradients at the selected scale. D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60(2):91-110, 2004.

  35. Steps 1-3 are the same; Step 4 is modified. Take a 41 x 41 patch at the given scale, centered at the keypoint, and normalized to a canonical direction. PCA-SIFT Yan Ke and Rahul Sukthankar, “PCA-SIFT: A More Distinctive Representation for Local Image Descriptors”, Computer Vision and Pattern Recognition, 2004

  36. Instead of using weighted histograms, concatenate the horizontal and vertical gradients (39 x 39) into a long vector. Normalize vector to unit length. PCA-SIFT 2 x 39 x 39 = 3042 vector

  37. PCA-SIFT Reduce the dimensionality of the vector using Principal Component Analysis (PCA) e.g., from 3042 to 36 Some times, less discriminatory than SIFT. PCA K x 1 N x 1

  38. SURF: Speeded Up Robust Features Speed-up computations by fast approximation of (i) Hessian matrix and (ii) descriptor using “integral images”. What is an “integral image”? Herbert Bay, Tinne Tuytelaars, and Luc Van Gool, “SURF: Speeded Up Robust Features”, European Computer Vision Conference (ECCV), 2006.

  39. Integral Image The integral image IΣ(x,y) of an image I(x, y) represents the sum of all pixels in I(x,y) of a rectangular region formed by (0,0) and (x,y). . Using integral images, it takes only four array references to calculate the sum of pixels over a rectangular region of any size.

  40. SURF: Speeded Up Robust Features (cont’d) Approximate Lxx, Lyy, and Lxy using box filters. Can be computed very fast using integral images! (box filters shown are 9 x 9 – good approximations for a Gaussian with σ=1.2) derivative approximation derivative approximation

  41. SURF: Speeded Up Robust Features (cont’d) In SIFT, images are repeatedly smoothed with a Gaussian and subsequently sub-sampled in order to achieve a higher level of the pyramid.

  42. SURF: Speeded Up Robust Features (cont’d) Alternatively, we can use filters of larger size on the original image. Due to using integral images, filters of any size can be applied at exactly the same speed! (see Tuytelaars’ paper for details)

  43. SURF: Speeded Up Robust Features (cont’d) • Approximation of H: Using DoG Using box filters

  44. SURF: Speeded Up Robust Features (cont’d) • Instead of using a different measure for selecting the location and scale of interest points (e.g., Hessian and DOG in SIFT), SURF uses the determinant of to find both. • Determinant elements must be weighted to obtain a good approximation:

  45. SURF: Speeded Up Robust Features (cont’d) • Once interest points have been localized both in space and scale, the next steps are: • (1) Orientation assignment • (2) Keypoint descriptor

  46. SURF: Speeded Up Robust Features (cont’d) • Orientation assignment Circular neighborhood of radius 6σ around the interest point (σ = the scale at which the point was detected) 600 window x response y response Haar wavelets (responses weighted with Gaussian) side length = 4σ dx dy Can be computed very fast using integral images!

  47. SURF: Speeded Up Robust Features (cont’d) • Orientation assignment • - The image is convoluted with two first-order Haar wavelets. • - The filter responses at certain sampling points around the keypoint are represented as a vector in a two-dimensional space. • - A rotating window of 600 is used to sum up all vectors within its range, and the longest resulting vector determines the orientation.

  48. SURF: Speeded Up Robust Features (cont’d) Sum the response over each sub-region for dx and dy separately. To bring in information about the polarity of the intensity changes, extract the sum of absolute value of the responses too. Feature vector size: 4 x 16 = 64 • Keypoint descriptor (square region of size 20σ) 4 x 4 grid

  49. SURF: Speeded Up Robust Features (cont’d) SURF-128 The sum of dx and |dx| are computed separately for points where dy < 0 and dy >0 Similarly for the sum of dy and |dy| More discriminatory!

  50. SURF: Speeded Up Robust Features Has been reported to be 3 times faster than SIFT. Less robust to illumination and viewpoint changes compared to SIFT. K. Mikolajczyk and C. Schmid,"A Performance Evaluation of Local Descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, 2005.

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