I have calculated 128 size vector for each keypoint in an image.
let, I1 is original image, I2 is 45 degree rotated image.
I got 130 keypoints for I1 and 104 keypoints for I2.
i.e. 128x130 and 128x104.
I calculated euclidean distance between one keypoint of I1 and all keypoints of I2. so I got again euclidean distance matrix of size 128x104.
Now I need to choose nearest keypoint from this euclidean distance matrix. How I can select minimum distance 128 size vector out of 128 x 104 sized matrix?
Hi.
Go through this papers for your solution
Tuzel, Oncel, Fatih Porikli, and Peter Meer. "Region covariance: A fast descriptor for detection and classification." Computer Vision–ECCV 2006. Springer Berlin Heidelberg, 2006. 589-600.
Juan, Luo, and Oubong Gwun. "A comparison of sift, pca-sift and surf." International Journal of Image Processing (IJIP) 3.4 (2009): 143-152.
I suggest you to use SURF approach instead of SIFT.
Here is the MATLAB implementation of SURF.
http://www.mathworks.com/matlabcentral/fileexchange/28300-opensurf--including-image-warp-
Regards
Ehtesham
In addition to AZM's explanation,
Assume you have l1=[X1,X2,X3] with 3 and l2=[Y1,Y2] with 2 keypoints, where Xi and Yi are vectors with 128 elements. Then you compute the distance between l1 and l2 as:
d(i,j)=|Xi-Yj| , i=1...3, j=1...2
where, d in our example will be a matrix with 3*2 row and col. After obtaining d, it is easy to find the closesr keypoint to keypoint i , by simply finding the min value in row i.
Notice that the distance between two vectors i.e. X=[x1,x2,...x128] and Y=[y1,y2,...,y128] given by:
|X-Y|=\sum{ |xi-yj| } for all i,j={1,2,...128}
You can also reduce the number of keypoints by following the paper:
"Key point reduction in SIFT descriptor used by subtractive clustering"
Hope it helps you.
sort the element row wise only by using the sorting command in matlab
Sort the matrix of Euclidean distances, get the first sorting element index.
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