Colourful Language: Searching for the Rainbow Eleanor MACLURE

London College of Communication, University of the Arts London

ABSTRACT The aim of the project was to produce a visual investigation into the relationship between colour and language. The methodology for the project involved a generative approach, where the eleven basic colour terms were inputted into Google Image Search. The first thirty images from the search results were used as a foundation to analyse using digital manipulation. The results of the project present a visual representation of each colour term, showing not only the variety of responses to the names of colours but also the degree of consensus across the range of images.

1. INTRODUCTION The philosopher Wittgenstein famously asked ‘How do I know that this color is red? —It would be an answer to say: I have learnt English.’ (Batchelor, 2000 pp.91) But how do we know what is red? In our today’s digital age perhaps a more pertinent response would be: ‘Google it’.

In many cases the Internet has become our primary source of information, the foundation of our knowledge economy. Largely inclusive and participatory, as an entity it represents the culmination of everything that has been written, posted and uploaded, with each new contribution slightly altering the shape of the whole. But how does Google know what is red? Tags, SEO and complex algorithms now create the precedence for the information we receive when we search online. Search engines are able to dictate the type of information we consume, influencing our perception of what is important with the ranking of their search results.

The use of Google Image Search as a tool to provide a visual interpretation of colour terms began as no more than an exercise in understanding. It proved to be an effective process and it became apparent that the search results were generating a fascinating body of images, providing an insight into both the fluid nature of the Internet and the relationship between the search terms and the colours in the images retrieved by Google.

2. METHODOLOGY When it became clear that Google Image Search had potential be a valuable tool for a visual investigation into colour and language, I felt it was important to devise a structured methodology while remaining appropriate to the scope of my design-based course. In this instance a generative approach was highly applicable as Google Image Search could be used as an input/output system.

English was chosen as the language for the project as I am a native speaker, was using a UK based IP address and ISP with Google UK as a default and because English is still the dominant language on the Internet both in terms of content generate and the native language of users. (Anon, 2013)

To create my initial set of colour terms to use for the process I employed the eleven basic colour terms in English as defined by Berlin & Kay (1969). They offered a clearly defined set of widely used colour terms, representing a range of both spectral, non-spectral colours. While there have been criticisms of bias in the methodology used by Berlin & Kay, these arguments were deemed not relevant to the methodology of the project.

Before beginning the process I cleared my browser history and deleted any cookies, so that the results would not be influenced by my Internet history. This was followed by inputting each of the eleven basic colour terms into Google Image Search with the addition of the word ‘colour’. The inclusion of the word colour in the search terms was an undesirable but an unfortunate consequence of a number of high profile celebrities who feature colour terms as part of their names. This created significant distortions in the image results for a small number of the colour terms in the initial trials of the process, without adding anything of value to the comparison. It should also be noted that for consistency I used the English spellings of the words 'grey' and 'colour' throughout, rather than the American English spellings 'gray' and 'color'.

Once each search had been completed I took, without exception, the first thirty images from the search results. When developing the methodology for the project thirty was considered to be a suitable number for the study, as it would generate a reasonable quantity of images without creating an excessive time burden for analysis.

Based on a series of earlier trials, a sequence of digital manipulation techniques were used to visually analyse the colour content of the image results. Each process was applied to the images in isolation rather than cumulatively. The original images were first converted to jpegs and were given the Adobe 1998 ICC colour profile. They were then converted to gifs so that they could be indexed using Colourphon (www.colourphon.co.uk), resulting in a 9 x 9 grid of dominant colours. The jpegs were blurred with a specific and constant amount of Gaussian blur (a radius of 80 pixels) in Photoshop and finally, also using Photoshop the RGB values for the pixels in each image were averaged to give a solid block of colour.

3. RESULTS & DISCUSSION The methodology for the project resulted in a combined total of 1320 images, 120 for each of the basic colour terms. As a visual investigation, creating a body of images rather than generating numerical data or observations, the results were originally presented in book form.

The contents were arranged by colour term, then grouped by process, beginning with the original results, and were presented in the sequence they ranked in the Google Image Search results. This meant the images could be compared both by hue and process, allowing the reader to appreciate how the colours and images transformed and mutated with each of the different processes applied, as they are transformed from clear shapes into a solid, uniform block of colour. The complete set of results is available to view online: http://issuu.com/Eleanorbydesign/docs/searching_for_the_rainbow/79.

As this study was produced as a visual research project, using design methodologies to investigate the relationship between colour and language, there was no formal data analysis conducted on the results. However there is much scope for further extension of the project, particularly the potential for analysis of RGB values for the images. While this would clearly add more weight and academic rigour to the results of the project, at the time it was

beyond the scope of my course. To have numerical data measured against accepted colour standards, supporting the visual output of the project may also benefit other researchers and make a more valid contribution to the field of colour naming.

However, one of the key aims at the outset of my main investigation was to differentiate this project from other studies in the same field through the creation of a strong visual component to the outputs of the research. The project provides a bridge between purely artistic studies of colour and scientific investigations in the field, striking a balance between aesthetic experiments and objective data. While there are always areas for improvement in this respect I view this aspect of the outcome as successful.

3.1 Extensions - further image manipulation As the use of Google Image Search proved fruitful I extended this line of enquiry. Working with the 1320 images already generated I considered methods which might produce further visual insight into the distribution of colours within the image results.

All of the images for each process and within each colour term were layered with 30% transparency to demonstrate the combined effect of the colours in each image. It allowed the areas saturated with the colour used as the search term to be seen more clearly and to see how that particular colour is distributed throughout the images.

A sample of the centre area of the averaged images was taken and enlarged. This produced a value for the culmination of the averages in the sample. It acted as a collective representation of that particular colour term, mediated both by Google and Photoshop.

In addition to this, images were created for each of the colour terms by taking a vertical, one pixel wide section through the layered images and stretching it across a wider area. This allowed a greater visual impression of the layering and presented the colour composition of the image in an alternative way. The full results for this part of the project are hosted by Issuu and are available to view online at: http://issuu.com/Eleanorbydesign/docs/transforming_the_rainbow/11

Collectively, these further visual experiments represented a conclusion to this particular line of investigation. While having created some striking and appealing imagery, the method of applying additional digital processes to the images presented a limited opportunity for further insight. However it is clear that the use of Google Image Search, and other search engines, as research tools has great potential to generate insights, certainly in colour naming, if not beyond.

3.2 Extensions - widening the search Also building on the methods outlined in section 3, I extended the process by expanded the search to commonly known, but less widely used colour terms. Although this criteria is not as clearly defined as the eleven basic colour terms and therefore subject to the influence of personal bias, it was useful to extend the process to include a greater variety of colour terms. The terms were all monolexemic, but many were descriptive, such as 'cream' and 'peach'.

As before, the colour names were used as search terms in Google and the first thirty images from the ranking were extracted in the order they appeared in the results. The images were presented in book form with the colour terms ordered sequentially by hue, so it was possible to compare the results of different searches or similarly coloured images.

The images were not subjected to any additional digital processes as this aspect study was specifically concerned with the comparison of Google Image Search results.

This method allowed for subtle distinctions to be observed between colour terms that are often regarded as interchangeable, for example wine, claret, maroon and burgundy. It also allowed for similarities to be seen between colours that are generally thought of as more disparate. The complete set of image results can be viewed online at: http://issuu.com/Eleanorbydesign/docs/looking_for_hue/21

The outcome of a Google Image Search may not provide a conclusive answer for the visual interpretation of colour terms, but as the results show, colour is rarely, if ever, definitive. Everyone’s idea of what a particular colour is, may equate to many subtly different shades. However, in all but a few cases, notably puce, there is a degree of consistency across the search results. While it is widely understood that both colour perception and colour naming are subjective, this methodology has produced a body of images defined by the process of colour naming but created in a real-world, non-experimental context. The degree of variation seen within the colour range of the images is a reflection of how each colour term is used in the vernacular.

4. CONCLUSION As a body of work this project presents a visual representation of each of the eleven basic colour terms, mediated by Google, and functions as a snapshot of colour on the Internet that due to it’s constantly shifting nature, can never be replicated exactly. It has allowed me to capture an interpretation of colour terms across a medium, which is still relatively democratic and where content is currently less regulated.

The results of each search add to the collective understanding of what a colour term represents, and it is possible for anyone, through their own contributions to the Internet, to influence that understanding to a degree. Although they are translated through Google’s algorithms, collectively, the images retrieved from internet searches represent the particular level of understanding of colour and colour terms by those posting them, tagging them or writing the accompanying text. This method is but one of the many possible ways of exploring the relationship between colour and language but it one that is truly a reflection of our digital age.

REFERENCES Anon., 2013. W3Techs. Web Technology Surveys. Available online,

http://w3techs.com/technologies/overview/content_language/all. Accessed: 19/04/13.

Batchelor, D., 2000. Chromophobia. London: Reaktion. Berlin, B. & Kay, P., 1969. Basic colour terms: their universality and evolution. Stanford :

Center for the Study of Language and Information. Colourphon, 2008. Available online, http://www.colourphon.co.uk. Accessed: 16/12/10.

Eleanor Maclure E-mail: eleanorbydesign@hotmail.com

Website: http://www.eleanormaclure.co.uk Blog: http://eleanormaclure.wordpress.com/