A computer‐assisted colour selection system based on aesthetic measure for colour harmony and fuzzy logic theory

An aesthetic measure based approach for constructing a colour design/selection system is proposed in this article. In this model, an image data base for the relationships between the psychological preference of customers and clothing colour tones is built using the membership functions of a fuzzy set, and an aesthetic measure calculation method based on colour harmony is also proposed. In addition, a skin colour detection theory is proposed to construct a skin colour detection program to detect the skin colour of a customer, which is then taken as the major colour in matching the skin, polo shirt, and(or) pant colours to select the best colour combination. Integrating the skin colour detection theory, colour harmony theory, aesthetic measure method, and fuzzy set theory, a program is constructed to build an aesthetic measure based colour design/selection system. With the aid of this system, one can get proper cloth colours to match his/her skin colour and image requirement by starting with inputting one's colour photo, catching image with a camera, or inputting R, G, B values of his/her skin. The theoretical results for the ranks of clothing colours proposed by the system are examined with the experimental results and the result shows they are very close, suggesting that the proposed colour selection system is acceptable. Although the selection of clothing colours is taken as an example to specify the methodology, it can also be used to develop a system for other products. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 411–423, 2008     

Comparison of real colour gamuts using a new reflectance database

A large set of data, comprising the spectral reflectances of real surface colours, has been accumulated. The data comprise 16 groups with different materials and include 85,879 measured spectra. From these data, CIELAB colorimetric coordinates were calculated under CIE illuminant D50 and the CIE 1931 standard colorimetric (2°) observer. Several published colour gamuts including those developed by Pointer and ISO reference colour gamut [ISO Graphic Technology Standard 12640‐3:2007] were compared using the present data set. It was found that the Pointer gamut is smaller than the new real data in most of the colour regions. The results also showed that the ISO reference colour gamut is larger than the new real accumulated data in most regions. The present finding indicates that there is a need to derive a new colour gamut based on the newly accumulated data for common applications. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 442–451, 2014     

Lightness and hue contrast effects in surface (fabric) colours

Simultaneous contrast effects on lightness and hue in surface colours were investigated. Test colours, surrounded by induction colours, were matched by colours surrounded by neutral gray. The matching colours were selected from a series of samples that varied in either lightness or hue respectively. The lightness experiments were carried out by a panel of 20 observers on 135 test/induction colour combinations. The hue experiments were conducted on 51 test/induction colour combinations by a panel of eight observers. The lightness of the test colour was found to decrease linearly with the lightness of the induction colour, regardless of the hue of the induction colour. The magnitude of the lightness contrast effect in fabric colours was found to be about one‐quarter of that found in CRT display colours in a previous study. The hue contrast effect found in this study followed the opponent‐colour theory. Two distinctly different regions could be identified when the hue difference was plotted against hue‐angle difference between the induction colour and the test colour. The slope of the line in the region where the hue of the induction colour is close to the test colour was much larger than the slope in the other region, indicating that the hue contrast effect was more obvious when the induction colour was close to the test colour. © 2006 Wiley Periodicals, Inc. Col Res Appl, 32, 55–64, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20285     

On the colours dichromats see

Dichromatic colour vision is commonly believed to be a reduced form of trichromatic colour vision (referred to as the reductionist principle). In particular, the colour palette of the dichromats is believed to be a part of the colour palette of the trichromats. As the light‐colour palette differs from the object‐colour palette, the dichromatic colour palettes have been derived separately for light‐colours and object‐colours in this report. As to light‐colours, the results are in line with the widely accepted view that the dichromatic colour palettes contain only two hues. However, the dichromatic object‐colour palettes have proved to contain the same six component colours which constitute the trichromatic object‐colour palette (yellow, blue, red, green, black and white). Moreover, all the binary and tertiary combinations of the six component colours present in the trichromatic object‐colour palette also occur in the dichromatic object‐colour palettes. Yet, only five of the six component colours are experienced by dichromats as unitary (unique) object‐colours. The green unitary colour is absent in the dichromatic object‐colour palettes. The difference between the dichromatic and trichromatic object‐colour palettes arises from the fact that not every combination of the component‐colour magnitudes occurs in the dichromatic object‐colour palettes. For instance, in the dichromatic object‐colour palettes there is no colour with the strong green component colour. Furthermore, each achromatic (black or white) component colour of a particular magnitude is combined with the only combination of the chromatic components. In other words, the achromatic component colours are bound with the chromatic component combinations in dichromats. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 112–124, 2014     

Colour and tolerance of preferred skin colours on digital photographic images

Skin‐tone has been an active research subject in photographic colour reproduction. There is a consistent conclusion that preferred skin colours are different from actual skin colours. However, preferred skin colours found from different studies are somewhat different. To have a solid understanding of skin colour preference of digital photographic images, psychophysical experiments were conducted to determine a preferred skin colour region and to study inter‐observer variation and tolerance of preferred skin colours. In the first experiment, a preferred skin colour region is searched on the entire skin colour region. A set of nine predetermined colour centers uniformly sampled within the skin colour ellipse in CIELAB a*b* diagram is used to morph skin colours of test images. Preferred skin colour centers are found through the experiment. In a second experiment, a twice denser sampling of nine skin colour centers around the preferred skin colour center determined in the first experiment are generated to repeat the experiment using a different set of test images and judged by a different panel of observers. The results from both experiments are compared and final preferred skin colour centers are obtained. Variations and hue and chroma tolerances of the observer skin colour preference are also analysed. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013     

A study of colour emotion and colour preference. Part II: Colour emotions for two‐colour combinations

Eleven colour‐emotion scales, warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, harmonious–disharmonious, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike, were investigated on 190 colour pairs with British and Chinese observers. Experimental results show that gender difference existed in masculine–feminine, whereas no significant cultural difference was found between British and Chinese observers. Three colour‐emotion factors were identified by the method of factor analysis and were labeled “colour activity,” “colour weight,” and “colour heat.” These factors were found similar to those extracted from the single colour emotions developed in Part I. This indicates a coherent framework of colour emotion factors for single colours and two‐colour combinations. An additivity relationship was found between single‐colour and colour‐combination emotions. This relationship predicts colour emotions for a colour pair by averaging the colour emotions of individual colours that generate the pair. However, it cannot be applied to colour preference prediction. By combining the additivity relationship with a single‐colour emotion model, such as those developed in Part I, a colour‐appearance‐based model was established for colour‐combination emotions. With this model one can predict colour emotions for a colour pair if colour‐appearance attributes of the component colours in that pair are known. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 292–298, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20024     

Colour words and their uses

Popular usage of colour words as parts of speech obey certain rules according to whether they are population dependent and whether use demands a degree of colour vision. The word green refers to that colour most of us see, recognize and categorize as being of the colour called green. But, colours and colour words are to do with emotion as well as perception. What can we learn from the greatest writers, artists and musical composers; how do they, for example, regard green? From them we learn that we perceive colours with our ears as well as our eyes and, in an emotional sense, a colour word means or is associated with just what the writer intends. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 111–113, 2015     

Computational production of colour harmony. Part 1: A prototype colour harmonization tool

Although web page and computer interface developers generally have little experience in generating effective colour schemes, colour selection appears rarely in user interface design literature, and there are few tools available to assist in appropriate choice of colours. This article describes an algorithmic technique for applying colour harmony rules to the selection of colour schemes for computer interfaces and web pages. Our software implementation of this approach—which we term the Colour Harmoniser—adapts and extends classical colour harmony rules for graphical user interfaces, combining algorithmic techniques and personal taste. A companion article presents the experimental evaluation of the system presented here. Our technique applies a set of rules for colour harmony to specific features of the interface or web page to create abstract colour schemes; the user then modifies the overall colour cast, saturation, and light–dark distribution, producing colourings that are both harmonious and usable. We demonstrate experimentally that the software is relatively simple to use and produces colourings that are well‐received by humans. In this article, we define a fitness function that numerically evaluates the colour harmony of a user interface and underpins a genetic algorithm for creating harmonious schemes. We show how abstract, hue‐independent, colour schemes may be mapped to real colour schemes, leaving the abstract colour harmony unchanged, but accommodating the developer's personal preferences for overall colouring, light–dark contrast, and saturation. This abstract/concrete separation automates the creation of harmonious schemes and allows unskilled developers to express their aesthetic preferences using simple direct manipulation controls. © 2011 Wiley Periodicals, Inc. Col Res Appl, 38, 203–217, 2013.     

A cross‐cultural colour‐naming study: Part II—Using a constrained method

This study aimed to investigate the differences in colour naming between the English (British) and Mandarin (Taiwanese) languages. A constrained method was employed, with 20 British and 20 Chinese adults. All the experiments were conducted under an artificial daylight, using 1526 colours from the Natural Color System (NCS). Each subject was asked to find the colour(s) corresponding to basic names, modifiers, and secondary names in terms of one colour (focal colour) or a colour region (colour volume). Little difference in chromaticness and hue was found between the two languages, but a systematic discrepancy was found in blackness. Because this could have been caused by different surrounds, i.e., gray and white walls used for the British and Chinese experiments, respectively, a verification experiment was carried out using a panel of ten Taiwanese subjects against a gray surround. The results proved that the lightness difference found earlier was indeed caused by the surround. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 193–208, 2001     

Colour category foci of Munsell colour spectra revealed by two computational methods

Colour names and psychophysical colour categories play an important role on human communication. For several application areas from computer vision to Internet shopping, it would be useful to manage colour information using methods of computational colour naming in a similar manner as people do in their everyday life. In this study, we applied two computational methods, the nonnegative matrix factorization and self‐organizing maps, to derive basic colours from a spectral database of Munsell colours, and a subset of it. The subset was generated to include only the most saturated samples of each Munsell Hue and Munsell Value pair of the original database. Using both the methods and both the databases, we calculated the sets of 3, 4, 6, and 8 basis vectors to represent the focal colours of colour categories. Colour names of the calculated focal colours were investigated using the results by Sturges and Whitfield as a reference. Nonnegative matrix factorization yields calculated colours more compatible with human basic colours, but the spectra generated by self‐organizing maps are more similar to natural spectra as their shape is smoother. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

A study of colour emotion and colour preference. Part III: Colour preference modeling

In this study three colour preference models for single colours were developed. The first model was developed on the basis of the colour emotions, clean–dirty, tense–relaxed, and heavy–light. In this model colour preference was found affected most by the emotional feeling “clean.” The second model was developed on the basis of the three colour‐emotion factors identified in Part I, colour activity, colour weight, and colour heat. By combining this model with the colour‐science‐based formulae of these three factors, which have been developed in Part I, one can predict colour preference of a test colour from its colour‐appearance attributes. The third colour preference model was directly developed from colour‐appearance attributes. In this model colour preference is determined by the colour difference between a test colour and the reference colour (L*, a*, b*) = (50, ?8, 30). The above approaches to modeling single‐colour preference were also adopted in modeling colour preference for colour combinations. The results show that it was difficult to predict colour‐combination preference by colour emotions only. This study also clarifies the relationship between colour preference and colour harmony. The results show that although colour preference is strongly correlated with colour harmony, there are still colours of which the two scales disagree with each other. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 381–389, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20047     

The architectural colour design process: An evaluation of sequential media via semantic ratings

In recent studies, contextual situations of applied colours are compared to colours presented as samples or chips. Findings of such studies point out different results in terms of similarities or differences between the evaluations of isolated/abstract colours and contextualized situations. Architectural and spatial contexts have their own characteristics regarding colouring criteria, so it is of great importance to examine the architectural/spatial colouring process from this point of view. This study explores this process by investigating the consistency of semantic ratings of four sequential stages of the architectural colour design process, namely, colour chips/samples, abstract compositions, perspective drawings and 3D models. The architectural context for the study was a simple interior space. Fifteen different colour schemes were applied on the four media representing the stages. Subjects rated the 15 sets against seven bipolar, five‐step semantic differential scales. The scales consisted of harmonious‐discord, pleasant‐unpleasant, comfortable‐uncomfortable, spacious‐confined, static‐dynamic, exciting‐calming and extroverted‐introverted. Findings indicated that there are significant associations between the evaluations of the abstract compositions, the perspective drawings and the 3D models; however, the evaluations of colour chips are significantly different than the others. The medium effect observed mostly between abstract and contextualized media. Additionally, factor analysis showed that pleasantness, harmony, spaciousness and comfort are connected in the evaluations of contextual situations, while pleasantness and harmony differ from spaciousness and comfort in the evaluations of colour chips and abstract compositions. The factor of activity (arousal) (dynamism, excitement, and extroversion) stays the same for all four media. It is also found that different colour characteristics are determinative over different media. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

A method for exploring word-colour associations

Strong associations exist between colours and concepts or words. Understanding these associations, sometimes referred to as colour emotions, is important for effective use of colour in art and design. Traditionally the relationships have been systematically explored in experiments where participants scale colours according to bipolar adjectives such as warm-cool. In this article, a method for exploring the relationships between words and colours is suggested and is demonstrated. A psychophysical experiment is described where participants select colours based on words. The data are used to show that many similarities between the word-colour relationships for UK and Chinese participants although some interesting differences are also revealed. The method makes explicit the observation that there is not a one-to-one relationship between words and colours. The method could be used to explore word-colour relationship for specific words and participant groups or could be used to generate ground-truth data for testing methods for automatically generating the word-colour relationships.     

Age effects on colour emotion,preference, and harmony

Two psychophysical experiments were carried out to investigate whether or not colour emotion responses would change with the advance of the viewer's age. Two forms of stimuli were used: 30 single colours (for Experiment 1) and 190 colour pairs (for Experiment 2). Four word pairs, warm/cool, heavy/light, active/passive, and like/dislike, were used to assess colour emotion and preference in Experiment 1. In Experiment 2, harmonious/disharmonious was also used in addition to the four scales for Experiment 1. A total of 72 Taiwanese observers participated, including 40 (20 young and 20 older) for Experiment 1 and 32 (16 young and 16 older) for Experiment 2. The experimental results show that for single colours, all colour samples were rated as less active, less liked, and cooler for older observers than for young observers. For colour combinations, light colour pairs were rated as less active and cooler for older observers than for young observers; achromatic colour pairs and those consisting of colours in similar chroma were rated as cooler, less liked and less harmonious for older observers than for young observers. The findings may challenge a number of existing theories, including the adaptation mechanism for retaining consistent perception of colour appearance across the lifespan, the modeling of colour emotion based on relative colour appearance values, and the additive approach to prediction of colour‐combination emotion. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

A study of colour emotion and colour preference. Part I: Colour emotions for single colours

This article classifies colour emotions for single colours and develops colour‐science‐based colour emotion models. In a psychophysical experiment, 31 observers, including 14 British and 17 Chinese subjects assessed 20 colours on 10 colour‐emotion scales: warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike. Experimental results show no significant difference between male and female data, whereas different results were found between British and Chinese observers for the tense–relaxed and like–dislike scales. The factor analysis identified three colour‐emotion factors: colour activity, colour weight, and colour heat. The three factors agreed well with those found by Kobayashi and Sato et al. Four colour‐emotion models were developed, including warm–cool, heavy–light, active–passive, and hard–soft. These models were compared with those developed by Sato et al. and Xin and Cheng. The results show that for each colour emotion the models of the three studies agreed with each other, suggesting that the four colour emotions are culture‐independent across countries. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 232–240, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20010     

A zonohedral approach to optimal colours

This article demonstrates that the CIE XYZ colour solid is a zonoid. An approximating zonohedral colour solid is constructed explicitly from a set of generating vectors, which are integrals of colour‐matching functions over narrow intervals of the visible spectrum. The zonohedral approach yields an intuitive, constructive proof of the Optimal Colour Theorem: the reflectance function of an optimal colour takes on only the values 0 or 1, with at most two transition wavelengths. In addition, zonohedral techniques can simplify computations: for example, optimal colours can be found without calculating transition wavelengths. Finally, zonohedra provide a simple, unified approach to colour space and eliminate much of the confusion arising from chromaticity diagrams. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013     

Effect of observer metamerism on colour matching of display and surface colours

We studied the individual variability of asymmetric metameric colour matching between computer displays and object colour stimuli in conditions typical for the surface colour industries. Using two different computational techniques, we assessed the contribution of observer metamerism to this variability. In the studied conditions of spatially separated computer display and surface colour stimuli, this contribution was found to be insignificant for all colours but neutrals. In the chromaticness plane, the range of matches made by different observers practically coincides with the range of matches made by an individual observer. Consequently, we conclude that in the task of matching spatially separated display and surface colours, the range of matches made by a group of observers cannot be determined from variations in their colour‐matching functions, and thus the paradigm of the Standard Deviate Observer is shown to be inapplicable to the studied conditions. We suggest that individual variability in these conditions is governed by mechanisms of chromatic discrimination, and can be modeled by advanced colour difference formulae with suitably adjusted parametric coefficients. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 346–359, 2008     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.