I have an image
A which I have divided into
4 x 4 subband images. For a given patch
P1 in image
A, I need to find another patch
P2 in the same image
A which is
most similar: have same texture.
Till now my appraoch is to create feature vectors
Fi for all
i patches and using Euclid's distance formula find out which patch's feature vector is closest to given patch P1's feature vector.
Currently I have added following features:
- Mean & Standard Deviation of brightness (using L channel of LAB colorspace)
- Mean & Standard Deviation of color values in A channel of LAB colorspace
- Mean & Standard Deviation of color values in B channel of LAB colorspace
Although I am getting similar patches but I still think the matching can be improved if I incorporate more prominent features (which I'm unaware of).
Following are my queries:
I have a doubt whether this is a good way to compare color between two patches.
Please suggest some more good features which can be helpful to get a proper differentiation.
Although most of my images are proper but are there any restrictions/drawbacks with the approach if image is dark and/or noisy? If yes, is there a good alternative approach or feature(s)?