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Spectral Spatial Gradients (SSGs) have been suggested as features invariant to illumination variation for color-based recognition and indexing. While SSGs have a greater degree of invariance than Spectral Gradients (SGs), they may have a reduced discrimination power between objects since they use only spatial changes of object reflectance. The approach presented is to use the framework of SGs and SSGs to create a recognition method which is invariant to varying illumination without discarding the reflectance distribution information. Two techniques are used to extract the varying illumination. The first is to assume that low-frequency SG variation is due to illumination. The second method uses regions with matching SSGs to get local estimates of illumination. Once the local illumination variation has been extracted, its effect on the SGs can be removed and the resulting Adapted Spectral Gradients (ASGs) have a greater power of discrimination for some objects than SSG features. Experimental results demonstrate cases where ASGs show improved performance.