Estimation of fuzzy sets for computational colour categorization

Colour is an important visual cue for computer vision applications. However, until recently, the automatic assignment of names to image regions has not been widely used due to the nonexistence of a general computational model for colour categorization. In this article we present a model for colour naming based on fuzzy‐set theory, in which each of the 11 basic colour terms defined by Berlin and Kay 1 is modeled as a fuzzy set with a characteristic function that assigns a membership value to the category to any colour sample. The model is based on combining two well‐known functions, a sigmoid and a Gaussian, to define a membership function for colour categories. It is denoted here as the sigmoid–Gaussian function and it fulfills a set of properties that make it adequate to this purpose. The characteristic functions for each colour category have been fitted to data obtained from a psychophysical experiment and the model has been tested on the Munsell colour array to show its validity. The results obtained indicate that our approach can be very useful as a first step to expand the use of colour‐naming information in computer vision applications. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 342–353, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20042     

Augmenting basic colour terms in english

In an unconstrained colour naming experiment conducted over the web, 330 participants named 600 colour samples in English. The 30 most frequent monolexemic colour terms were analyzed with regards to frequency, consensus among genders, response times, consistency of use, denotative volume in the Munsell and OSA colour spaces and inter‐experimental agreement. Each of these measures served for ranking colour term salience; rankings were then combined to give a composite index of basicness. The results support the extension of English inventory from the 11 basic colour terms of Berlin and Kay to 13 terms by the addition of lilac and turquoise. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 32–42, 2016     

A cross‐cultural colour‐naming study. Part I: Using an unconstrained method

Colour naming by panels of British and Taiwanese subjects (speaking English and Mandarin, respectively) was used to study colour categorization, and the results applied to investigate differences of usage between the two languages. Fifty British and 40 Chinese subjects took part in experiments using an unconstrained method with 200 ISCC‐NBS colour samples. Data analysis was performed to calculate the frequency and codability of each colour name in each group and subgroup. These names were then grouped using 7‐category and 4‐category methods to find the culture and gender differences. It was confirmed that the 11 basic names found by Berlin and Kay were the most widely used for both languages. The results showed a close agreement between the two languages in terms of colour categories, but a large discrepancy in the use of secondary names due to cultural differences. The cross‐cultural comparison revealed a clear pattern of the linkage between language and concepts of colour. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 40–60, 2001     

Differences in color naming and color salience in Vietnamese and English

The accepted model of color naming postulates that 11 “basic” color terms representing 11 common perceptual experiences show increased processing salience due to a theorized linkage between perception, visual neurophysiology, and cognition. We tested this theory, originally proposed by Berlin and Kay in 1969. Experiment 1 tested salience by comparing unconstrained color naming across two languages, English and Vietnamese. Results were compared with previous research by Berlin and Kay, Boynton and Olson, and colleagues. Experiment 2 validated our stimuli by comparing OSA, Munsell, and newly rendered “basic” exemplars using colorimetry and behavioral measures. Our results show that the relationship between the visual and verbal domains is more complex than current theory acknowledges. An interpoint distance model of color‐naming behavior is proposed as an alternative perspective on color‐naming universality and color‐category structure. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 113–138, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10131     

A colour harmony model for two‐colour combinations

This study investigates harmony in two‐colour combinations in order to develop a quantitative model. A total of 1431 colour pairs were used as stimuli in a psychophysical experiment for the visual assessment of harmony. These colour pairs were generated using 54 colours selected systematically from CIELAB colour space. During the experiment, observers were presented with colour pairs displayed individually against a medium gray background on a cathode ray tube monitor in a darkened room. Colour harmony was assessed for each colour pair using a 10‐category scale ranging from “extremely harmonious” to “extremely disharmonious.” The experimental results showed a general pattern of two‐colour harmony, from which a quantitative model was developed and principles for creating harmony were derived. This model was tested using an independent psychophysical data set and the results showed satisfactory performance for model prediction. The study also discusses critical issues including the definition of colour harmony, the relationship between harmony and pleasantness, and the relationship between harmony and order in colour. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 191–204, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20208     

Color and culture: Innovations and insights since Basic Color Terms—Their Universality and Evolution (1969)

Fifty years ago, in 1969, Berlin & Kay published Basic Color Terms—Their Universality and Evolution and set in‐motion a large‐scale systematic research program for studying color naming and categorization across first‐language speakers from different ethno‐linguistic societies. While it is difficult to gauge the impact a research program can make over 50‐years, many linguists, anthropologists, cognitive scientists, and perceptual psychologists consider the Berlin & Kay book as one of the top‐most influential works in cross‐cultural studies not only of color linguistics, but of cognition and language more generally. Today reverberations from the Berlin & Kay (1969) research program continue to resonate through recently available data sets that are being examined with new quantitative analysis methods and modeling approaches. Here we review the origins of the Basic Color Terms phenomenon, and note a few of the numerous directions from which on‐going related work continues to bring forth interesting results in the color categorization arena.     

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     

A cross‐cultural colour‐naming study. Part III—A colour‐naming model

A colour‐naming model was developed to categorize volumes for each of the 11 basic names in CIELAB colour space. This was tested with three different sets of data for two languages (English and Mandarin), derived from extensive colour categorization experiments. The performance of the model in predicting colour names was satisfactory, with an average prediction error of 8.3%. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 270–277, 2001     

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 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     

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     

Uniform colour spaces based on the DIN99 colour‐difference formula

Several colour‐difference formulas such as CMC, CIE94, and CIEDE2000 have been developed by modifying CIELAB. These formulas give much better fits for experimental data based on small colour differences than does CIELAB. None of these has an associated uniform colour space (UCS). The need for a UCS is demonstrated by the widespread use of the a*b* diagram despite the lack of uniformity. This article describes the development of formulas, with the same basic structure as the DIN99 formula, that predict the experimental data sets better than do the CMC and CIE94 colour‐difference formulas and only slightly worse than CIEDE2000 (which was optimized on the experimental data). However, these formulas all have an associated UCS. The spaces are similar in form to L*a*b*. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 282–290, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10066     

Investigation of texture effect on visual colour difference evaluation

The texture effect on visual colour difference evaluation was investigated in this study. Five colour centers were selected and textured colour pairs were generated using scanned textile woven fabrics and colour‐mapping technique. The textured and solid colour pairs were then displayed on a characterized cathode ray tube (CRT) monitor for colour difference evaluation. The colour difference values for the pairs with texture patterns are equal to 5.0 CIELAB units in lightness direction. The texture level was represented by the half‐width of histogram, which is called texture strength in this study. High correlation was found between texture strength and visual colour difference for textured colour pairs, which indicates that an increasing of 10 units of texture strength in luminance would cause a decreasing of 0.25 units visual difference for the five colour centers. The ratio of visual difference between textured and solid colour pairs also indicates a high parametric effect of texture. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 341–347, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Cross‐regional comparison of colour emotions Part II: Qualitative analysis

During the colour perception process, an associated feeling or emotion is induced in our brains, and this kind of emotion is known as colour emotion. In Part I of this study, a quantitative analysis of the cross‐regional differences and similarities of colour emotions as well as the influence of hue, lightness, and chroma on the colour emotions of the subjects from Hong Kong, Japan, and Thailand, was carried out. In Part II, colour emotions of the subjects in any two regions were compared directly using colour planners showing the effect of the lightness and the chroma of colours. The colour planners can help the designers to understand the taste and feelings of the target customers and facilitate them to select suitable colours for the products that are intended to be supplied in different regions. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 458–466, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20063     

Evaluation of the crispening effect using CRT‐displayed colour samples

In this study, the crispening effect was clearly observed when 38 neutral‐coloured sample pairs with only lightness differences were assessed under 5 neutral backgrounds of different lightness values. The sample pairs are CRT‐based colours, and they are selected along the CIELAB L* axis from 0 to 100. The magnitude of colour difference of each pair is 5.0 CIELAB units. The visual assessment results showed that there is a very large crispening effect. The colour differences of the same pair assessed under different backgrounds could differ by a factor of up to 8 for a sample pair with low lightness. The perceived colour difference was enlarged when the lightness of a sample pair was similar to that of the background. The extent of crispening effect and its quantification are discussed in this investigation. The performances of five colour‐difference equations were also tested, including the newly developed CIEDE2000. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 374–380, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20045     

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     

Determining colour gamuts of digital cameras and scanners

The determination of the colour gamuts of colour reproduction media is an important aspect of both understanding them in isolation and using them in the context of a colour reproduction system. Although this is well understood for output colour reproduction media, a solution for input media is not to be had in a simple way. To this end, in this article, we propose a method based on simulating the responses of an input medium to given spectral power distributions. We then determine the gamut of an input medium on the basis of having a set of spectra that covers the majority of all possible spectra, knowing a medium's quantized responses to them, and then determining a boundary beyond which the medium does not produce variation in its responses. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 59–68, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Cross‐regional comparison of colour emotions Part I: Quantitative analysis

Colour emotion is a feeling or emotion induced in our brains when we look at a colour. In this article, the colour emotional responses obtained by conducting visual experiments in different regions, namely Hong Kong, Japan and Thailand, using a set of 218 colour samples are compared using a quantitative approach in an attempt to study the influence of different cultural and geographical locations. Twelve pairs of colour emotions described in opponent words were used. These word pairs are warm–cool, light–dark, deep–pale, heavy–light, vivid–sombre, gaudy–plain, striking–subdued, dynamic–passive, distinct–vague, transparent–turbid, soft–hard, and strong–weak. These word pairs represent the fundamental emotional response of human beings toward colour. The influences of lightness and chroma were found to be much more important than that of the hue on the colour emotions studied. Good correlations of colour emotions among these three regions in East Asia were found, with the best ones for colour emotion pairs being light–dark and heavy–light. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 451–457, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20062     

Analysis of experiments to determine individual colour preference

Finding an efficient way to understand individual colour preference is important to researchers and designers. This article compares three research strategies to test individual colour preference including two research experimental environments (online and laboratory) and two research methods (multiple choice for N‐alternative‐forced‐choice and multiple choice for rank‐order). Three psychophysical experiments have been carried out. Participants were presented with six colour patches (red, orange, yellow, green, blue, and purple) arranged in a random order on a computer display. In the first two experiments (Online experiment and Laboratory experiment I), participants were asked to indicate which colour square they prefer most; in the third experiment (Laboratory experiment II), participants were asked to rank their colour preferences of the six colour patches. The similarity between the results obtained from two experimental environments provides some validation for the online protocol and suggests that online experiments could be used more often. Pairwise comparisons for individual colour preference between genders and nationalities were carried out, and it was found that male and female responses were significantly different; but there was no statistical significance between Chinese and UK participants. The results from Monte Carlo simulations suggested that the rank‐order method should be preferred for individual colour preference studies involving small numbers of participants (especially less than 15 participants).