Efficient object recognition using color quantization.

摘要

A simplification of the color histogram indexing algorithm is proposed and analyzed. Instead of taking a histogram consisting of hundreds of colors, each input image is first quantized to only a few colors (on the order of ten) and the feature vector is generated by taking a histogram of this smaller space. This increases the efficiency of the system by orders of magnitude. We also proposed that this would reduce the effects of lighting change on the algorithm and that this would be a better model for the human object recognition mechanism than the algorithm combined with color constancy alone.; In support of the contention that this may be a better human model; a psychophysical experiment was conducted. The bit-depth of human color memory was shown to lie between 3 and 4 bits, corresponding to 8 to 16 color categories when a color is remembered for five seconds. This experiment created a bridge between the worlds of the psychophysical results and the computer testbed.; The research showed that quantization can occasionally compensate for small lighting changes, but that the compensation is highly database-dependent and erratic. However, quantization always produced a much more efficient system and generally did not substantially reduce the accuracy.; The results of this work were threefold. First, human color memory is relatively poor, indicating that a system incorporating quantization will be far closer to mimicking human abilities than the usual implementation. Second, quantization alone is insufficient to perform color constancy in most cases. Third, with or without a color constant pre-processor, our results consistently showed that quantization has little effect on accuracy when using more than sixteen bins. Object recognition accuracy degrades substantially as the number of color categories drops below six. From 10 categories to 256, accuracy is essentially unchanged. Quantization is a very efficient way to reduce the computational complexity and storage requirements of this algorithm without substantially affecting its object recognition accuracy.