Image spectral clustering method based on quick selection of landmark points
A landmark point and spectral clustering technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve the problems of image data distribution information loss, large error in image sparse representation, large amount of computation and storage, etc. Improve processing speed, improve accuracy, overcome the effect of noise
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[0038] The present invention will be further described below with reference to the accompanying drawings.
[0039] Refer to the attached figure 1 , the specific steps of the present invention are as follows.
[0040] Step 1, read all the images to be spectrally clustered.
[0041] Step 2: Calculate the nearest neighbor graph of the image to be spectrally clustered.
[0042] Using Silverman's rule of thumb, compute the bandwidth of the radial basis kernel function for all neighbor graphs of all images read.
[0043] The specific steps described for utilizing Silverman's rule of thumb are as follows:
[0044] Step 1, according to the following formula, calculate the standard deviation of all the images read in the same feature dimension:
[0045]
[0046] where σ h Represents the standard deviation of all images read in the h-th feature dimension, represents the square root operation, N represents the total number of all images read, ∑ represents the summation operation,...
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