The role of massive color quantization in object recognition
Psychophysical experiments inspire a more complete analysis of the effect of quantization on a modified version of the histogram indexing method of object recognition. We derive an equation that describes how the amount of quantization and number of features kept affects the recognition accuracy. The equation shows that quantization from 2/sup 24/ colors to 15 colors has a negligible effect on accuracy. A simulation shows that large numbers of objects cause a corresponding decrease in accuracy, but that keeping more features can increase the accuracy even for massive quantization. An object recognition experiment with real data shows dramatically better results when quantization is used, indicating that massive color quantization can provide some invariance to lighting conditions.