[Zz] PCA-SIFT & gloh

Http://blog.sina.com.cn/s/blog_5d793ffc0100g240.html

 

Later, sift had two extensions that used the PCA concept.
1 PCA-SIFT

The PCA-SIFT has the same sub-pixel location (sub-pixel), scale, and dominant orientations as the standard sift, but when the description is calculated in step 1, it uses 41 × 41 image spots around the feature points to calculate its principal component, and uses a PCA-SIFT to reduce the original 2 × 39 × 39 vectors to 20 dimensions, to achieve a more accurate representation.

A step to create a PCA-SIFT description sub:

1) Calculate or load the Projection Matrix

2) detection of key points

3) Multiply the projection matrix to project the image spots around the key points.

1.1 generation of PCA-SIFT Projection Matrix

1.11 select a series of representative images and detect all the key points of these images

1.12 for each key point:

1> select a 41X41 pixel image around it

2> calculate the vertical and horizontal gradient to form a vector with a size of 39 × 39 × 2 = 3042.

3> put these vectors into a K * 3042 matrix A. k is the number of key points detected.

4> covariance of matrix A: A = A-mean a cov A = ATA

5> calculate the feature value and feature vector of cov

6> select the first n feature vectors. The projection matrix is a matrix of N x 3042 composed of these feature vectors.

7> N can be a fixed value based on experience or can be dynamically selected based on feature values.

8> the projection matrix is only calculated once and stored

1.2 create a description sub-

1.21 input: position and direction of the key points in the scale space

1.22 extracts a 41 × 41 image spot around the key point at a given scale and rotates it to its main direction.

1.23 calculates the horizontal and vertical gradients of 39 × 39 to form a vector with a size of 3042.

1.24 multiply the pre-computed projection matrix n × 3042 by the vector

1.25 this generates a PCA-SIFT description of n size

Comparison Between Two sift and PCA-SIFT

2.1 sift:

Dimension: 128

Disadvantages: high dimension, incomplete affination

Advantage: requires less empirical knowledge and is easy to develop

2.2 PCA-SIFT:

Dimension: variable. We recommend 20 or fewer dimensions.

Disadvantage: the projection matrix requires a series of representative images. This matrix only applies to these images.

Advantage: the non-Immutable low-dimensional storage is retained, which greatly reduces the computing time.

2 gloh (gradient location-orientation histogram)

Change the location bins of the 4x4 chess board in the original sift to the 17 Location bins of the affine concentric circle, and calculate the gradient direction histogram (16 gradient directions ), therefore, a total of 16x17 = 272 dimensions are used, and then PCA is used to reduce it to 128 dimensions. Therefore, the representation method is as simple as that of sift.