10: Image (Chromaticity) Segmentation

Image segmentation using the different types of channels and thresholds may not work in cases where the wanted colors are not obvious. Instead, we look at the RG Chromaticity space to mask our image.

RG Chromaticity Space

Chromaticity Segmentation relies on the RG Chromaticity space, a two-dimensional representation of color that removes information regarding the intensity value of the colors. We are looking at the proportion of the different color channels and map it using the normalized RGB space. The RG Chromaticity only have r and g because the b can be solved using the last equation.

To demonstrate the formula, let us consider 3 colors, pure red [255, 0, 0], a darker shade of red [128, 0, 0], and a mixed color [150, 50, 250]. The first 2 colors have the same values since

r = 255/(255+0+0) = 128/(128+0+0) = 1, g = 0, b = 0

For the last color, more calculations need to be done

r = 150/(150+50+250)=1/3, g = 1/9, b = 5/9

From these calculations, r and g can be plotted on the rg chromaticity space.

RG Chromaticity Space & 2d Histogram Representation

Considering the image of the bean bags, running the calculations per color channel, masking the values onto the chromaticity space, and getting a 2d histogram, 5 main clusters of colors are visible.

Selecting a specific color patch

Let’s try using this to segment the orange bag. This is called Parametric Segmentation. Parametric segmentation now requires us to fit a gaussian probability distribution using this mask. This probability distribution would dictate which pixel belongs to the color of interest. To do this, we need to compute for the Mean and Standard Deviation of our object of interest.

Creating a mask based on the patch

Setting the probability as a threshold, we can create a mask to segment the orange bag. Further morphological operations may be conducted to fill in the gaps.



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Nisarg Nigam

Nisarg Nigam

MS Data Science student at Asian Institute of Management