Optimality of the basic colour categories for classification.

Abstract

Categorization of colour has been widely studied as a window into human language and cognition, and quite separately has been used pragmatically in image-database retrieval systems. This suggests the hypothesis that the best category system for pragmatic purposes coincides with human categories (i.e. the basic colours). We have tested this hypothesis by assessing the performance of different category systems in a machine-vision task. The task was the identification of the odd-one-out from triples of images obtained using a web-based image-search service. In each triple, two of the images had been retrieved using the same search term, the other a different term. The terms were simple concrete nouns. The results were as follows: (i) the odd-one-out task can be performed better than chance using colour alone; (ii) basic colour categorization performs better than random systems of categories; (iii) a category system that performs better than the basic colours could not be found; and (iv) it is not just the general layout of the basic colours that is important, but also the detail. We conclude that (i) the results support the plausibility of an explanation for the basic colours as a result of a pressure-to-optimality and (ii) the basic colours are good categories for machine vision image-retrieval systems.