Colors, Shapes, and Database Starts 01/24/2018

This week has been a whirlwind, so much is getting done in bits and pieces that the end of the week was spent trying to finish things up and put it all together. The first thing that has to be done, was perfecting my scoring metric for the images, with the inclusion of shapes.

Perfecting my Chi-Score

Color Theory

Meeting with my adviser for this project led me to reconsidering how the program scores color. Now, instead of using a confusing metric for RGB values, the program examines the Hue value of a color. This uses a wheel of the hue values, and examines how far apart colors are on this hue wheel. When 3 colors are the maximum amount of distant from each other, 120 degrees, the program will give them a perfect score.120 degrees rule

The program automatically sorts the image into three approximate colors. If three colors are really close together, they will get a worse score. Below are examples of how the program has scored some color schemes.

new colors the program likes
Color schemes that had better color scores comparatively.
new colors the program hates
Color schemes that had bad color scores comparatively.

As you can see, none of the color schemes seem particularly “better” or “worse” than one another, although you can see the similarities between the colors in the color schemes that ranked the lowest.

It is perfectly plausible that the opinion on different colors being better is wrong, however, I believe I will gain more insight into this when the student survey goes out.


Having different sizes is important in an image, however, I needed an exact score for this. To determine the ideal large/medium/small shape ratio, I turned toward the golden ratio.

golden score

Without addressing the asymmetry of the image, I can tell that having two large blobs, 4 medium blobs, and 3 small blobs should be considered ideal. This image does NOT contain any tiny blobs, which is something I have always disliked in images.

However, this seems to give too much precedence to image that form almost no cells whatsoever. Below we see an example of a better score that formed.

Sizes : {small=34, big=1, tiny=217, medium=4}

This got a score of 0.38! One of the better scores out there, despite being mostly empty!

There are two things I am going to do to address this, I am going to release the restriction on tiny blobs, and I am going to say that having 0 – 1 big blobs is actually bad, and it is only good when there is over 2 big blobs!

Since the image can have up to 10 big blobs in it by how we defined blobs being “big”, I will say that 2 – 5 big blobs is an ideal range.

From here, I came up with a score that I found much better, and it chose images that had more shapes, more going on, and were not as empty as the previous metric.

These are some of the images that scored better for size out of 35 randomly generated image. The top left had a score of 0.75 which was significantly higher than the rest that we had seen.

As we can see though, these don’t necessarily have good color schemes. It is my hope that combining these two metrics will make a new metric that will overall be more appealing.

Database Start

Our database has been started, and we are still calibrating it to be easier to read/write things to before we can move on to really implementing its usage!

Next week our goal is entirely to get a usabel servey out.


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