NICE: A Computational Solution to Close the Gap from Colour Perception to Colour Categorization

Abstract

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms.

Pink for Girls, Red for Boys, and Blue for Both Genders: Colour Preferences in Children and Adults

“…Hue angles were binned into nine perceptually relevant categories. The discrete ranges for each hue category were based on the results from Parraga and Akbarinia (2016), who compiled a physiologically plausible colour categorization model and identified, by using the method of adjustment, which hue ranges matched specific colour terms (for more information, see Jonauskaite et al, 2016;Parraga & Akbarinia, 2016, and our online supplement). From these nine categories, we selected and regrouped (in the case of Pink/Purple) only the hue categories that were pertinent for the research question: Red (Hue angle = 346°-40°, lightness < 70), Blue (Hue angle = 166°-275°), Pink/Purple (Hue angle = 275°-346°, or Hue angle = 346°-40° and lightness ≥ 70); and binned the remaining hues into a single category: Other (the remaining hue angles and/or when chroma < 5 for achromatic choices).…”

Variations in normal color vision. VII. Relationships between color naming and hue scaling

“…This relativism could arise because verbal categories are presumably also shaped by interaction and communication among observers (Jameson & Komarova, 2009, Lindsey, Brown, Brainard & Apicella, 2015, Steels & Belpaeme, 2005), so that categories are influenced by both perception and language [e.g. (Cibelli, Xu, Austerweil, Griffiths & Regier, 2016)], as well as a variety of other factors or decision rules at the various levels of representing and categorizing color (Cropper, Kvansakul & Little, 2013, Parraga & Akbarinia, 2016). Consequently this may weaken the potential links between color perception and color naming.…”

Stripping #The Dress: the importance of contextual information on inter-individual differences in colour perception

“…dark and light strips, and dark and light parts of the full ambiguous Dress image. Additionally, to test how colour categories match the circular hue angles, we binned hues into nine perceptually relevant categories(Jonauskaite et al, 2016;Parraga & Akbarinia, 2016): red (346°-40°], orange (40°-72°], yellow (72°-105°], yellow-green (105°-130°], green (130°-166°], green-blue (166°-220°], blue (220°-275°], and purple (275°-346°]. All these categories had chroma values above 5, while the last category -achromatic -had chroma values below 5 of any hue angle.…”