Communication efficiency of color naming across languages provides a new framework for the evolution of color terms.

Languages vary in their number of color terms. A widely accepted theory proposes that languages evolve, acquiring color terms in a stereotyped sequence. This theory, by Berlin and Kay (BK), is supported by analyzing best exemplars ("focal colors") of basic color terms in the World Color Survey (WCS) of 110 languages. But the instructions of the WCS were complex and the color chips confounded hue and saturation, which likely impacted focal-color selection. In addition, it is now known that even so-called early-stage languages nonetheless have a complete representation of color distributed across the population. These facts undermine the BK theory. Here we revisit the evolution of color terms using original color-naming data obtained with simple instructions in Tsimane', an Amazonian culture that has limited contact with industrialized society. We also collected data in Bolivian-Spanish speakers and English speakers. We discovered that information theory analysis of color-naming data was not influenced by color-chip saturation, which motivated a new analysis of the WCS data. Embedded within a universal pattern in which warm colors (reds, oranges) are always communicated more efficiently than cool colors (blues, greens), as languages increase in overall communicative efficiency about color, some colors undergo greater increases in communication efficiency compared to others. Communication efficiency increases first for yellow, then brown, then purple. The present analyses and results provide a new framework for understanding the evolution of color terms: what varies among cultures is not whether colors are seen differently, but the extent to which color is useful.

Color statistics of objects, and color tuning of object cortex in macaque monkey.

We hypothesized that the parts of scenes identified by human observers as "objects" show distinct color properties from backgrounds, and that the brain uses this information towards object recognition. To test this hypothesis, we examined the color statistics of naturally and artificially colored objects and backgrounds in a database of over 20,000 images annotated with object labels. Objects tended to be warmer colored (L-cone response > M-cone response) and more saturated compared to backgrounds. That the distinguishing chromatic property of objects was defined mostly by the L-M post-receptoral mechanism, rather than the S mechanism, is consistent with the idea that trichromatic color vision evolved in response to a selective pressure to identify objects. We also show that classifiers trained using only color information could distinguish animate versus inanimate objects, and at a performance level that was comparable to classification using shape features. Animate/inanimate is considered a fundamental superordinate category distinction, previously thought to be computed by the brain using only shape information. Our results show that color could contribute to animate/inanimate, and likely other, object-category assignments. Finally, color-tuning measured in two macaque monkeys with functional magnetic resonance imaging (fMRI), and confirmed by fMRI-guided microelectrode recording, supports the idea that responsiveness to color reflects the global functional organization of inferior temporal cortex, the brain region implicated in object vision. More strongly in IT than in V1, colors associated with objects elicited higher responses than colors less often associated with objects.

Color naming across languages reflects color use.

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane', a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane' had relatively low communicative efficiency, and the Tsimane' were less likely to use color terms when describing familiar objects. Color-naming among Tsimane' was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.

What predicts the strength of simultaneous color contrast?

The perceived color of a uniform image patch depends not only on the spectral content of the light that reaches the eye but also on its context. One of the most extensively studied forms of context dependence is a simultaneous contrast display: a center-surround display containing a homogeneous target embedded in a homogenous surround. A number of models have been proposed to account for the chromatic transformations of targets induced by such surrounds, but they were typically derived in the restricted context of experiments using achromatic targets with surrounds that varied along the cardinal axes of color space. There is currently no theoretical consensus that predicts the target color that produces the largest perceived color difference for two arbitrarily chosen surround colors, or what surround would give the largest color induction for an arbitrarily chosen target. Here, we present a method for assessing simultaneous contrast that avoids some of the methodological issues that arise with nulling and matching experiments and diminishes the contribution of temporal adaption. Observers were presented with pairs of center-surround patterns and ordered them from largest to smallest in perceived dissimilarity. We find that the perceived difference for two arbitrarily chosen surrounds is largest when the target falls on the line connecting the two surrounds in color space. We also find that the magnitude of induction is larger for larger differences between chromatic targets and surrounds of the same hue. Our results are consistent with the direction law (Ekroll & Faul, 2012b), and with a generalization of Kirschmann's fourth law, even for viewing conditions that do not favor temporal adaptation.

The role of chromatic variance in modulating color appearance.

Chromatictarget patches embedded in a chromatically variegated surround appear less saturated than when they are embedded in an achromatic uniform surround (Brown & MacLeod, 1997), which can be construed as either a form of gamut expansion for targets on uniform surrounds or as a form of gamut compression for targets on variegated surrounds.Ekroll, Faul, and Niederée (2004) suggested that the difference in perceived chromaticity on the two surrounds is caused by a layered scene decomposition, wherein the increased saturation of targets on homogenous surrounds is attributed to a decomposition of a target patch into a chromatically saturated transparent layer overlying an achromatic background.Here, we report asymmetric matching data that show the perceived chromaticity difference observed on the two surrounds depends on the particular direction of chromatic variation applied to the variegated surround. If the chromatic variegated surround has the same or a similar hue to that of the target and the saturation variation of the surround is large compared to the saturation of the target the gamut expansion effect is also large. However, if the variegated surround has a different hue than the hue of the target, the perceived chromaticity difference is small and largely does not depend on the variation in saturation of the surround. These results suggest that a layered scene representation cannot fully explain the gamut expansion effect and suggest that a chromatically tuned contrast gain control mechanism may contribute to the difference in perceived color of targets on achromatic homogeneous surrounds and chromatically variegated surrounds.

Chromaticity space for illuminant invariant recognition.

In this paper an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with non-uniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colours under the International Commission on Illumination (CIE) standard illuminants and obtained a good performance. It was also tested for colour based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard colour spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant colour based object recognition and skin detection.

Extending "color constancy" outside the visible region.

In this paper, the results of an investigation of the possibility of extending "color constancy" to obtain illuminant-invariant reflectance features from data in the near-ultraviolet (UV) and near-infrared (IR) wavelength regions are reported. These features are obtained by extending a blackbody-model-based color constancy algorithm proposed by Ratnasingam and Collins [J. Opt. Soc. Am. A27, 286 (2010)] to these additional wavelengths. Ratnasingam and Collins applied the model-based algorithm in the visible region to extract two illuminant-invariant features related to the wavelength-dependent reflectance of a surface from the responses of four sensors. In this paper, this model-based algorithm is extended to extract two illuminant-invariant reflectance features from the responses of sensors that cover the visible and either the near-UV or near-IR wavelength. In this investigation, test reflectance data sets are generated using the goodness-fitness coefficient (GFC). The appropriateness of the GFC for generating the test data sets is demonstrated by comparing the results obtained with these data with those obtained from data sets generated using the CIELab distance. Results based upon the GFC are then presented that suggest that the model-based algorithm can extract useful features from data from the visible and near-IR wavelengths. Finally, results are presented that show that, although the spectrum of daylight in the near UV is very different from a blackbody spectrum, the algorithm can be modified to extract useful features from visible and near-UV wavelengths.

Illuminant spectrum estimation at a pixel.

In this paper, an algorithm is proposed to estimate the spectral power distribution of a light source at a pixel. The first step of the algorithm is forming a two-dimensional illuminant invariant chromaticity space. In estimating the illuminant spectrum, generalized inverse estimation and Wiener estimation methods were applied. The chromaticity space was divided into small grids and a weight matrix was used to estimate the illuminant spectrum illuminating the pixels that fall within a grid. The algorithm was tested using a different number of sensor responses to determine the optimum number of sensors for accurate colorimetric and spectral reproduction. To investigate the performance of the algorithm realistically, the responses were multiplied with Gaussian noise and then quantized to 10 bits. The algorithm was tested with standard and measured data. Based on the results presented, the algorithm can be used with six sensors to obtain a colorimetrically good estimate of the illuminant spectrum at a pixel.

Optimum sensors for color constancy in scenes illuminated by daylight.

The apparent color of an object within a scene depends on the spectrum of the light illuminating the object. However, recording an object's color independent of the illuminant spectrum is important in many machine vision applications. In this paper the performance of a blackbody-model-based color constancy algorithm that requires four sensors with different spectral responses is investigated under daylight illumination. In this investigation sensor noise was modeled as gaussian noise, and the responses were quantized using different numbers of bits. A projection-based algorithm whose output is invariant to illuminant is investigated to improve the results that are obtained. The performance of both of these algorithms is then improved by optimizing the spectral sensitivities of the four sensors using freely available CIE standard daylight spectra and a set of lightness-normalized Munsell reflectance data. With the optimized sensors the performance of both algorithms is shown to be comparable to the human visual system. However, results obtained with measured daylight spectra show that the standard daylights may not be sufficiently representative of measured daylight for optimization with the standard daylight to lead to a reliable set of optimum sensor characteristics.

Study of the photodetector characteristics of a camera for color constancy in natural scenes.

An algorithm is described to extract two features that represent the chromaticity of a surface and that are independent of both the intensity and correlated color temperature of the daylight illuminating a scene. For mathematical convenience this algorithm is derived using the assumptions that each photodetector responds to a single wavelength and that the spectrum of the illumination source can be represented by a blackbody spectrum. Neither of these assumptions will be valid in a real application. A new method is proposed to determine the effect of violating these assumptions. The conclusion reached is that two features can be obtained that are effectively independent of the daylight illuminant if photodetectors with a spectral response whose full width at half maximum is 80 nm or less are used.