Applied regression analysis and other multivariable methods
Applied regression analysis and other multivariable methods
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional Properties of Colour Co-occurrence Features for Lip Location and Segmentation
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Colour Image Texture Analysis: Dependence on Colour Spaces
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Improving Texture Pattern Recognition by Integration of Multiple Texture Feature Extraction Methods
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling multiple-classifier relationships using Bayesian belief networks
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Selective neural network ensemble based on clustering
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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.