Preliminary Color Cycle Order Ranking Results

Last month, I presented a preliminary analysis of ranking color sets using responses collected in the Color Cycle Survey. Now, I extend this analysis to look at color ordering within a given color set. For this analysis, the same artificial neural network architecture was used as was used before, except that batch normalization, with a batch size of 2048, was used after the two Gaussian dropout layers. Determining ordering turned out to be a slightly more difficult problem, in part because the data cannot be augmented, since the ordering, obviously, matters. However, due to the way the survey is structured, with the user picking the best of four potential orderings, there are three pairwise data points per response. The same set of responses was used, ignoring the additional responses collected since the previous analysis was performed (there are now ~10k total responses).

To maximize the information gleaned from the survey responses, the network was trained in four steps. The process started with a single network and ended with a conjoined network, as before, except the single network underwent three stages of training instead of one. First, the color set responses—the responses that were used in the previous analysis—were used to train the network for 50 epochs, to learn color representations. Next, the ordering responses were used with the data augmented with all possible cyclic shifts to train the network for an additional 50 epochs, to learn internal cycle orderings. Then, the non-augmented ordering responses were used to train the network for another 100 epochs, to learn the ideal starting color. Finally, the last layer of the network was replaced, as before, to make a conjoined network, and the new network was trained for a final 100 epochs, again with the non-augmented ordering responses. Continue reading

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Preliminary Color Cycle Set Ranking Results

Since I launched my color cycle survey in December, it has collected ~9.7k responses across ~800 user sessions. Although the responses are not as numerous as I’d like, there’s currently enough data for preliminary analysis. The data are split between sets of six, eight, and ten colors with ratios of approximately 2:2:1; there are fewer ten-color color set responses as I disabled that portion of the survey months ago, to more quickly record six- and eight-color color set responses. So far, I’ve focused on analyzing the set ranking of the six-color color sets, for which there are ~4k responses, using artificial neural networks. The gist of the problem is to use the survey’s pair-wise responses to train a neural network such that it can rank 10k previously-generated color sets; these colors sets each have a minimum perceptual distance between colors, both with and without color vision deficiency simulations applied.

As inputs with identical structure are being compared, a network architecture that is invariant to input order, i.e., one that produces identical output for inputs (A, B) and (B, A), is desirable. Conjoined neural networks1 satisfy this property; they consist of two identical neural networks with shared weights, the outputs of which are combined to produce a single result. In this case, each network takes a single color set as input and produces a single scalar output, a “score” for the input color set. The two scores are then compared, with the better scoring color set of the input pair chosen as the preferred set; put more concretely, the difference of the two scores is computed and used to calculate binary cross-entropy during network training. The architecture of the network appears in the figure below and contains 2077 trainable parameters.

Artificial Neural Network Architecture Diagram Continue reading

  1. Bromley, Jane, Isabelle Guyon, Yann LeCun, Eduard S├Ąckinger, and Roopak Shah. “Signature verification using a ‘Siamese’ time delay neural network.” In Advances in neural information processing systems, pp. 737-744. 1994. 

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Hilbert Curve Cake

Three years ago, I entered an Ashley Book of Knots Cake into the Johns Hopkins University Sheridan Libraries’ third annual Edible Book Festival. For this year’s contest, I figured I could apply my 3D-printed Hilbert curve microwave absorber research to craft a cake for Hans Sagan’s Space-Filling Curves book1 on the eponymous topic. Thus began an endeavor involving thermoplastic, silicone, and sugar.

Hilbert curve cake Continue reading

  1. H. Sagan, Space-Filling Curves (Springer-Verlag, 1994). ISBN: 9780387942650. DOI: 10.1007/978-1-4612-0871-6

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3D-Printed Tea Bag Holder

When readily available containers do not come in the desired form factor, 3D-printing can be quite useful. In this case, I wanted a tea bag holder that fit on a small ledge, allowed the tea bag labels to be read, and allowed the tea bags to be easily removed. Although there are some similar products available commercially that would fit the space, they either seemed a bit flimsy or looked to be a tight fit around the tea bags, which would make them more difficult to remove. Thus, I designed and 3D-printed a modular holder that can be stacked. The holder was printed out of PLA, and the design files are available.

Tea Bag Holder

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Color Cycle Survey

A previous post about randomly generating color sets with a minimum perceptual distance addresses the technical aspects of generating sets of colors that are visually distinct for those with normal color vision as well as for those with color vision deficiencies. However, it does not address the aesthetic aspect, which I will start to address here. To create an aesthetically pleasing color cycle—an ordered set of colors for visualizing categorical data—two aspects need to be addressed, the colors that are used and the order that they are used in. While one could take an ontological approach to this by trying to define a set of rules that make a pleasing color cycle, as is done by I Want Hue,1 such a method is error-prone and substantially biased toward the personal preferences of the drafter of the rules. Instead of using ontologies, an alternative approach that is gaining traction in many fields is to infer a pattern from a large data set using machine learning techniques. This is the approach I wish to pursue.

To this end, I’ve created a Color Cycle Survey. After presenting the user with an introduction, colorblindness questionnaire, and directions, the primary survey starts. In it, the user is presented with two color sets and is asked to choose the one that is, in the user’s opinion, more aesthetically pleasing. Then, four orderings of the chosen set are displayed, and the user again makes a selection to taste. This basic process is then repeated again and again, with sets of either six, eight, or ten colors. For the choice of color set, each set is presented ordered by hue, since this makes the two sets easier to compare than if they were randomly ordered (or ordered by RGB values). Only two sets are presented, to make for an easier choice. Additionally, a line plot or scatter plot rendering is shown with each color set. For the choice of ordering, four orders are presented, since multiple orderings are easier to compare than sets, since the colors are all the same. I considered asking the user to order the colors to taste, instead of presenting possible orderings, but I decided that while such an approach yields more information per response, it takes much longer and requires more effort, so each user will likely respond many fewer times. Thus, I went with the simpler approach.

If all goes well, I’ll amass a sizable data set from the survey, which will remain available for at least a few months. Once I have data to experiment with, I’ll work out the exact analysis method. In addition to generating an “optimal” color cycle, it would also be interesting to create a model that allows for additional constraints, such as being able to choose the first color or being able to choose the exact number of colors in the cycle. Once anonymized, I’ll release the survey data under a permissive license, probably CC BY 4.0 (I’m open to suggestions). Any generated color cycles will be release into the public domain via the CC0 public domain dedication.

  1. I Want Hue takes a fairly rudimentary approach to color vision deficiency simulation, which I find lacking; I personally have difficulty differentiating colors in the many of the sets it generates, even when its colorblind mode is turned on. It also doesn’t really address the ordering of the color set into a cycle.  

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