0.166 in volume Figure 3.5a shows a mondrian of color patches created by picking points in the rgb color space that fall Mto one such bin. As can be seen, the colors within this bin are dramatically different even in hue 4. A segmentation algorit based on histogram analysis in color space could potentially treat this bin as a sngle color (as the bn size is sufficiently small) so that a segmentation of the mondrian image of Figure 3.5a would be as indicated Figure 3.5b. Thus it appears that boundaries where perceptual color changes occur must be found before any cluster in color space can be interpreted as corresponding to a region in image space. This observation 'is the basis of our approach to color specification presented next.
3.3 Perceptual Color Specification by Categories
For the purposes of selection we propose that it is sufficient if a region could be
34 Data and Model-driven Selection using Color Regions separately accounted, as *in [88, 87]. Using the perceptual color, two adjacent color regions would be distinguished if their perceived colors were different, and this 'is
sufficient for data-driven selection. Because objects tend to obey color constancy under most changes in nation, their perceived color remains more or less the same thus making it sufficient also for model-driven selection. But can perceptual color be quantified at a? In general, several effects such as s taneous color
(referred to as specific color before), representing the filtered components of e intensity at a point or pixel along three wavelengths corresponding to the red, green and blue filters of the camera. Wn all possible triples are mapped into a 3-dimensional color space with axes standing for pure red, green and blue respectively, we obtain a color space that represents the entire spectrum of computer recordable colors. Such a color space must, therefore, be partitionable into subspaces where the color remains perceptually the same, and is distinctly different from that of neighboring subspaces. Such subspaces can be called color categories. Now, each pixel in a color image maps to a point 'in this color space, and hence will into one of these categories. The perceptual color of this pixel can, therefore, be specified
by this color category. To obtain the perceptual color of regions from the perceptual color of their constituent pixels, we observe the following. Although the individual pixels of an image color region may show considerable variation in their specific colors, the overall color of the region is fairly well-dete ed by the color of the majority of pixels (called dominant color henceforth). Therefore, the perceived color of a region can be specified by the color category corresponding to the dominant color in the region.
Since color categories capture the perceptual color, two adjacent color regions would belong to two different categories, making it possible, as we will show later,
to extract the different color regions. This can make category-based specification of color stable for data-driven selection. For model-driven selection, the color description of the object should remain stable. Since the specific color can change considerably with ging conditions, it may cause a change in the color category too. But by restricting the illumination changes and by han g effects of artifacts such as specularities and inter-reflections separately, as we will discuss later, the category-based specification can still be sufficient for model-driven selection.
The category-based specification of perceptual color (of pixels or regions) is a compromise between choosing the specific color (which is extremely unstable with respect to changes in ilumination conditions, etc. and surface color (whose recovery is hard). Also, since the perceptual categories depend on the color space and are independent of the 'image, they can be found 'in advance and stored in, say, a look-up table. Finally, a category-based description is in keeping wth the idea of perceptual categorization that has been explored extensively through psychophysical studies [10, 19, 129]. These studies concluded that although humans can discriminate between several thousand nuances of colors, psychophysically we seem to partition the color space into relatively few distinct qualitative color sensations or categories 139, 49].
3.3.1 Categorization of color space
The above discussion agued for the viability of an approach that recovers a color to within a category. Before this can be turned M'to a computational method of color recovery one needs to address the issue of how such categories may be found. Pre-vious. work on color categorization involved experiments of naming the color using a ted vocabulary, or identifying colors using the Munsell color charts [166]. But for computational color recovery, we need a way to convert the camera recordable red, green and blue components of colors into computer recordable perceptual color categories. This was done by performing some rather informal but extensive psy-chophysical experiments that systematically examined a color space and recorded the places where qualitative color changes occur, thus deter ' ' the number of distinct color categories that can be perceived. For this the hue-saturation-value
,6h 6s 6v number of bins number of categories
5 0.1 I 0.1 7200 I 220 I
Table 31: Parameters used for the quantization of color space.
color space was used as it specifies a given color in terms of its hue, purity and brilliance - attributes that have been found to give a perceptual description of color [80]. The details of these experiments are described in Appendix A and will not color space. The color category information was then s immarized in a color-look-up table. Although it is true that a finer level of quantization would have yielded more color categories given by the color-look-up table.