Measuring Perceptual Distance of Organismal Color Pattern using the Features of Deep Neural Networks

Abstract

A wide range of research relies upon the accurate and repeatable measurement of the degree to which organisms resemble one another. Here, we present an unsupervised workflow for analyzing the relationships between organismal color patterns. This workflow utilizes several recent advancements in deep learning based computer vision techniques to calculate perceptual distance. We validate this approach using previously published datasets surrounding diverse applications of color pattern analysis including mimicry, population differentiation, heritability, and development. We demonstrate that our approach is able to reproduce the biologically relevant color pattern relationships originally reported in these studies. Importantly, these results are achieved without any task-specific training. In many cases, we were able to reproduce findings directly from original photographs or plates with minimum standardization, avoiding the need for intermediate representations such as a cartoonized images or trait matrices. We then present two artificial datasets designed to highlight how this approach handles aspects of color patterns, such as changes in pattern location and the perception of color contrast. These results suggest that this approach will generalize well to support the study of a wide range of biological processes in a diverse set of taxa while also accommodating a variety of data formats, preprocessing techniques, and study designs.