k-means clustering on RGB or HSV scale?

Question

I want to segment an image but someone told me that the Euclidean distance for RGB is not as good as HSV -- but for HSV, as not all H, S, V are of the same range so I need to normalize it. Is it a good idea to normalize HSV and then do clustering? If so, how should I normalize on HSV scale?

Thanks

Answer 1

Firstly you need to know why HSV is more preffered than RGB in image segmentation. HSV separates color information (Chroma) and image intensity or brightness level (Luma) which is very useful if you want to do image segmentation. For example if you try to use RGB approach for a photo with sea as the background there is a big chance the dominant RGB component in the sea is not blue (usually because of shadow or illumination). But if you are using HSV, value is separated and you can construct a histogram or thresholding rules using only saturation and hue.

There is a really good paper that compared both RGB and HSV approach and I think it will be a good read for you -> http://www.cse.msu.edu/~pramanik/research/papers/2002Papers/icip.hsv.pdf