Combining color and spatial information for object recognition across illumination changes

One of the most widely used approaches in the context of object recognition across illumination changes consists in comparing the images by means of the intersection between invariant histograms. However, this approach does not provide satisfying results with limited image databases. We propose to cope with the problem of illumination changes by analyzing simultaneously the color components of the pixels and their spatial arrangement in the image. For this purpose, we introduce the chromatic co-occurrence matrices to characterize the relationship between the color component levels of neighboring pixels. In order to compare two images acquired under different illuminations, these matrices are transformed into adapted co-occurrence matrices that are determined so that their intersection is higher when the two images contain the same object lit with different illuminations than when they contain different objects.