What unsupervised learning algoritms might appropriate for color segmentation?

[CasperStaahl]

Based on the image below from the MI slides for unsupervised learning, It seems that the following are good candidates:

• MiniBatchKMeans
• DBSCAN
• Gaussian Mixture

[CasperStaahl]

Soft clustering is not useful! The image is already soft clustered... kinda. The points is I don't think it's going to be useful.

[CasperStaahl]

Autoencoders don't seem applicable.

[CasperStaahl]

"If speed is our main concern then FCM and KHM seem to be reasonable choices. However, if the quality of the solution is our main objective then population-based stochastic approaches represent viable options because they can, in general, avoid being trapped in local optima." Omran, M. G., Engelbrecht, A. P., & Salman, A. (2007). An overview of clustering methods. Intelligent Data Analysis, 11(6), 583-605.

[CasperStaahl]

My current idea is clustering model on the pixel of a given image every once in a while (using the previous model as a starting point). The idea is that there would be one cluster for every color on the whiteboard, including the color of the whiteboard. Then we can segment later pictures on the model trained before, and train the model as needed.

Further, the label of each cluster would be the average color, maybe with some post-processing.

[CasperStaahl]

Asking the user to make a drawing with the colors they intend to use might make this a lot simpler, you can then learn the clusters only once at the start.

[CasperStaahl]

The method of clustering is probably not that important, I think getting the overall architecture in place is key.

[CasperStaahl]

I did look at some papers and lecture notes, only doing a first quick pass:

• CS229 Lecture Notes
• Omran, Mahamed GH, Andries P. Engelbrecht, and Ayed Salman. "An overview of clustering methods." Intelligent Data Analysis 11.6 (2007): 583-605.
• Milligan, Glenn W., and Martha C. Cooper. "Methodology review: Clustering methods." Applied psychological measurement 11.4 (1987): 329-354.
• Rokach, Lior, and Oded Maimon. "Clustering methods." (2005): 321-352.
• Gaertler, Marco. "Clustering." Network analysis: Methodological foundations (2005): 178-215.
• Xu, Rui, and Donald Wunsch. "Survey of clustering algorithms." IEEE Transactions on neural networks 16.3 (2005): 645-678.

[CasperStaahl]

Conclusion

It seems that a form of clustering algorithm might be most appropriate for this task