Abstract
Biological color may be adaptive or incidental, seasonal or permanent, species- or population-specific, or modified for breeding, defense or camouflage. Although color is a hugely informative aspect of biology, quantitative color comparisons are notoriously difficult. Color comparison is limited by categorization methods, with available tools requiring either subjective classifications, or expensive equipment, software, and expertise. We present an R package for processing images of organisms (or other objects) in order to quantify color profiles, gather color trait data, and compare color palettes on the basis of color similarity and amount. The package treats image pixels as 3D coordinates in a “color space,” producing a multidimensional color histogram for each image. Pairwise distances between histograms are computed using earth mover’s distance, a technique borrowed from computer vision, that compares histograms using transportation costs. Users choose a color space, parameters for generating color histograms, and a pairwise comparison method to produce a color distance matrix for a set of images. The package is intended as a more rigorous alternative to subjective, manual digital image analyses, not as a replacement for more advanced techniques that rely on detailed spectrophotometry methods unavailable to many users. Here, we outline the basic functions of colordistance, provide guidelines for the available color spaces and quantification methods, and compare this toolkit with other available methods. The tools presented for quantitative color analysis may be applied to a broad range of questions in biology and other disciplines.
Highlights
IntroductionColor is an information-rich trait, and has provided countless insights in biology, including into camouflage, mimicry, pollination, signaling, mate attraction, pathogen infection, and thermoregulation (Cuthill et al, 2017; Liu & Nizet, 2009; Clegg & Durbin, 2000; Smith & Goldberg, 2015; Smith et al, 2016; Bechtel, Rivard & SánchezAzofeifa, 2002; Lev-Yadun et al, 2004; Pérez-De la Fuente et al, 2012; Stevens, Lown & Wood, 2014; Chiao et al, 2011; Brady et al, 2015; Troscianko et al, 2016)
In order to plot the flower in CIE Lab color space (Fig. 2B), we provide plotPixels with: (1) the path to the background-masked image, (2) lower and upper bounds for RGB pixels to ignore, (3) the color space in which to plot, and (4) the name of a standard reference white for RGB to CIE Lab conversion, since the image is stored in an RGB format
We provide brief guidelines for choosing between the different color spaces, binning methods, and distance metrics in colordistance, and discuss how colordistance differs from similar packages and methods
Summary
Color is an information-rich trait, and has provided countless insights in biology, including into camouflage, mimicry, pollination, signaling, mate attraction, pathogen infection, and thermoregulation (Cuthill et al, 2017; Liu & Nizet, 2009; Clegg & Durbin, 2000; Smith & Goldberg, 2015; Smith et al, 2016; Bechtel, Rivard & SánchezAzofeifa, 2002; Lev-Yadun et al, 2004; Pérez-De la Fuente et al, 2012; Stevens, Lown & Wood, 2014; Chiao et al, 2011; Brady et al, 2015; Troscianko et al, 2016). Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance. The resulting digital images are intended to mimic human vision, appropriate calibration and an understanding of these limitations can allow scientists to answer a much wider range of questions with this simpler data format (Troscianko & Stevens, 2015). Any objective categorization must account for the amount, distribution, classification, and variety of colors consistently across a set of images. Researchers must account for the limits of using digital images to answer questions about the visual systems of non-human animals. More comprehensive methods require expensive equipment, expertise, and coding skills, while more straightforward methods are tailored for specific studies, giving them a more limited scope
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