Comprehensive Colour Image Normalization

Abstract

The same scene viewed under two different illuminants induces two different colour images. If the two illuminants are the same colour but are placed at different positions then corresponding rgb pixels are related by simple scale factors. In contrast if the lighting geometry is held fixed but the colour of the light changes then it is the individual colour channels (e.g. all the red pixel values or all the green pixels) that are a scaling apart. It is well known that the image dependencies due to lighting geometry and illuminant colour can be respectively removed by normalizing the magnitude of the rgb pixel triplets (e.g. by calculating chromaticities) and by normalizing the lengths of each colour channel (by running the ‘grey-world’ colour constancy algorithm). However, neither normalization suffices to account for changes in both the lighting geometry and illuminant colour. In this paper we present a new comprehensive image normalization which removes image dependency on lighting geometry and illumination colour. Our approach is disarmingly simple. We take the colour image and normalize the rgb pixels (to remove dependence on lighting geometry) and then normalize the r, g and b colour channels (to remove dependence on illuminant colour). We then repeat this process, normalize rgb pixels then r, g and b colour channels, and then repeat again. Indeed we repeat this process until we reach a stable state; that is reach a position where each normalization is idempotent. Crucially this iterative normalization procedure always converges to the same answer. Moreover, convergence is very rapid, typically taking just 4 or 5 iterations. To illustrate the value of our “comprehensive normalization” procedure we considered the object recognition problem for three image databases that appear in the literature: Swain's database, the Simon Fraser database, Sang Wok Lee's database. In all cases, for recognition by colour distribution comparison, the comprehensive normalization improves recognition rates (the results are near perfect and in all cases improve on results reported in the literature). Also recognition for the composite database (comprising almost 100 objects) is also near perfect.