Activity 12 - Color Image Segmentation

From the early activities, thresholding an image is one way of segmenting the region of interest from the background. However, this cannot be easily applied for colored images with an obtained grayscale that is almost uniform. That is, the region of interest or the object of study has the same brightness as the background.

One way to solve this problem is to exploit the color information contained in the image. Many objects of important study can have distinct colors that can separate itself from its surroundings. However, since three-dimensional objects can have shading variations of its color, images are usually rendered in their normalized chromaticity coordinates, which describe its pure color information. Normalized chromaticity coordinates are obtained for each channel (RGB) by dividing the value for that channel with the sum of the values in three channels. As a consequence, the image can be represented by just two coordinates, usually r and g. An example of a normalized chromaticity coordinate space is shown below, with r and g as the x and y coordinates, respectively.

Normalized chromaticity coordinate space

From the normalized chromaticity coordinates, color image segmentation is applied by obtaining the probability in which a pixel or a point in the image belongs to the color distribution of the region or object of interest. With this in mind, there are two methods, which basically generates the histogram (distribution) of color of the region of interest: Parametric and Non-parametric probability estimation.

Parametric probability estimation assumes a Gaussian distribution of the color of the region of interest. By obtaining the statistics from the region of interest (mean and standard deviation), the probability of a pixel falling into this distribution is easily calculated. Of course, the total probability is based on the probability of both falling into the distribution of r and g. The joint probability is taken which is the product of the probability for r and the probability for g.

On the other hand, non-parametric probability estimation uses a more accurate description of the distribution of the color of the region of interest. The actual (2D) histogram of the r and g values of the region of interest is obtained. Given the values of the r and g values of a pixel, the pixel is replaced with the value in the histogram corresponding to the r and g values. This histogram backprojection method basically uses the histogram of the region of interest as a "look-up table."

Segmentation can have significant applications for studies regarding colored objects. One major study is about corals. As a demonstration, sample images are obtained and segmented with the region of interest as the corals. This can help in studying coral growth and, possibly, in connections with specific marine species studies.

Original and its binary image


Parametric probability estimation results and its binary image


Non-parametric probability estimation results and its binary image

This image shows a set of corals displayed. The coral that chosen to be segmented here is the red one. A patch from the image of the red coral was taken as the basis for which the normalized chromaticity coordinates distribution for both parametric and non-parametric probability estimation would be obtained. Notice from the binary image of the original that thresholding is not sufficient to highlight and segment only the red coral. Applying color image segmentation, both methods showed high detection of the red coral. This demonstrates a good segmentation result. However, there are still portions of the orange coral that is unintentionally segmented. The non-parametric method result suggests that it has a more robust method. It provides lower segmentation for the orange coral.

Original and its binary image


Parametric probability estimation results and its binary image


Non-parametric probability estimation results and its binary image

Color image segmentation is now demonstrated here for an image of a site of corals in the sea. In wanting to study the areas where the orange coral grows, a patch was chosen from this coral. Segmentation was done and from the results shown above, it is very effective in isolating the coral from the background. The non-parametric probability estimation result is also verified to be better than the parametric results, because it is much more specific in segmenting the orange coral.

For this activity, I would like to give myself a grade of 10 I obtained very desirable results. I also thought of, somehow, a significant object of study to be processed (corals).
I acknowledge our professor, Dr. Maricor Soriano, and my classmates, Winsome Chloe Rara and Mark Jayson Villangca for discussions regarding this activity.