Analysis and Modelling of Diversity Contribution to Ensemble-Based Texture Recognition Performance

The RGB colour space is prominent as a colour representation and display scheme, although a number of other colour spaces have been developed over the years each with its own advantages and shortcomings with regard to its usefulness for colour/texture recognition. However, the recent advent of multiple classifier systems provides the unique opportunity to exploit the diverse information encapsulated in the different colour representations in a systematic fashion. In this paper we propose the use of classifier combination schemes which utilise information from different colour domains. We subsequently use suitable measures to investigate the diversity of the information infused by the different colour spaces. Experiments with two 40-class colour/texture datasets show the benefit of our multiple classifier approach, and reveal the existence of strong correlations between the accuracy achieved and the diversity measures. Finally, we illustrate, using quadratic regression, that there is significant scope to build and explore further (potentially causal) models of the observed relations between ensemble performance and diversity metrics. Our results point towards the use of diversity along with other statistical measures as possible predictors of the ensemble behaviour.