A visual interpretation of basic colour termsthrough the use of Google Image Search
Searchingfor theRainbow
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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.’ But how do we know what is red? In today’s digital age perhaps a more pertinent response would be: ‘Google it’.
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For many the internet has become our primary source of information and knowledge. Inclusive and participatory, as an entity it represents the culmination of everything that has been posted and uploaded, with each new contribution slightly altering the shape of the whole. But how does Google know what is red? Tags, Search Engine Optimisation and complex algorithms now set the precedence for the information we receive when we search for something. Search engines are able to dictate the type of information we consume, influencing our perceptions of what is important with the ranking of their search results.
Introduction
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Process
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, to enable me to progress with other research, it was a means to an end. However, repetition of the process began to reveal both the changing nature of the internet and the similarities and differences between the colours in the images retrieved by each search. Although translated through Google’s algorithms, collectively, the images represent the level of understanding of colour and colour terms by those posting them, tagging them or writing the accompanying text.
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The methodology for this exercise involved inputting each of the eleven basic colour terms, defined by Berlin & Kay, into Google Image Search with the addition of the word ‘colour’. This concession was an undesirable but necessary consequence of the number of high profile celebrities with colour terms in their names. I then took (without exception) the first thirty images from the search results, as a foundation to work with and analyse using image manipulation processes. The images were indexed using Colourphon, resulting in a 9 x 9 grid of colour. They were blurred with a specific and constant amount of Gaussian blur 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.
Process
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As a body of work, the project presents a visual representation of each colour term, 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 shows not only the variety of responses to the names of colours but also the level of consensus across the range of images. It allows the viewer to appreciate how the colours and images transform and mutate with each of the different processes applied, as they move from defined forms, into indistinguishable shapes, to a solid, uniform block of colour. 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.
Conclusions
Pink
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pink Search Results
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pink Pixelated
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pink Blurred
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pink Colour Averaged
Red
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red Search Results
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red Pixelated
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red Blurred
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red Colour Averaged
Orange
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orange Search Results
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orange Pixelated
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orange Blurred
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orange Colour Averaged
Yellow
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Yellow Search Results
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Yellow Pixelated
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Yellow Blurred
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Yellow Colour Averaged
Green
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Green Search Results
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Green Pixelated
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Green Blurred
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Green Colour Averaged
Blue
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blue Search Results
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blue Pixelated
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blue Blurred
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blue Colour Averaged
Purple
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purple Search Results
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purple Pixelated
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purple Blurred
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purple Colour Averaged
Brown
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brown Search Results
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brown Pixelated
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brown Blurred
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brown Colour Averaged
Black
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black Search Results
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black Pixelated
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black Blurred
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black Colour Averaged
Grey
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Grey Search Results
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Grey Pixelated
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Grey Blurred
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Grey Colour Averaged
White
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white Search Results
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white Pixelated
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white Blurred
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