Recognising small colour changes with unsupervised learning, comparison of methods

Abstract

AbstractColour differentiation is crucial in machine learning and computer vision. It is often used when identifying items and objects based on distinct colours. While common colours like blue, red, green, and yellow are easily distinguishable, some applications require recognising subtle colour variations. Such demands arise in sectors like agriculture, printing, healthcare, and packaging. This research employs prevalent unsupervised learning techniques to detect printed colours on paper, focusing on CMYK ink (saturation) levels necessary for recognition against a white background. The aim is to assess whether unsupervised clustering can identify colours within QR-Codes. One use-case for this research is usage of functional inks, ones that change colour based on environmental factors. Within QR-Codes they serve as low-cost IoT sensors. Results of this research indicate that K-means, C-means, Gaussian Mixture Model (GMM), Hierarchical clustering, and Spectral clustering perform well in recognising colour differences when CMYK saturation is 20% or higher in at least one channel. K-means stands out when saturation drops below 10%, although its accuracy diminishes significantly, especially for yellow or magenta channels. A saturation of at least 10% in one CMYK channel is needed for reliable colour detection using unsupervised learning. To handle ink densities below 5%, further research or alternative unsupervised methods may be necessary.