Abstract
A new multiscale subspace method of feature extraction based on improved contourlet transform (ICT) and kernel spectral regression (KSR) is proposed. We construct the ICT according to the construction mode of contourlet transform and nonsubsampled contourlet transform (NSCT), ICI is built upon iterated nonsubsampled pyramids and subsampled directional filter banks to obtain directional multiresolution image representation. The ICT not only has the advantages of multi-resolution and fast calculation speed of contourlet transform but also has the advantages of low aliasing in the frequency domain of NSCT. KSR is a subspace learning method used to fast dimensionality reduction for multi-scale feature extracted. ICT–KSR, as a new feature extraction method, is applied to the inspection of metal surface defects in aluminum sheets and continuous casting slabs. The experimental results show that the proposed method performs better than the other methods. The best recognition rate of aluminum sheets and continuous casting slabs are up to 94.58% and 94.76%, respectively.
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