A hybrid fuzzy-genetic colour classification system with best colour space selection under dynamically-changing illumination

Authors:
Heesang Shin;Napoleon H. Reyes;Andre L. Barczak
Affiliations:
Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand;Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand;Institute of Information and Mathematical Sciences, Massey University, Auckland, New Zealand
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

This paper contributes in colour classification under dynamically changing illumination, extending further the capabilities of our previous works on Fuzzy Colour Contrast Fusion (FCCF), FCCF-Heuristic Assisted Genetic Algorithm (HAGA) for automatic colour classifier calibration and Variable Colour Depth (VCD). All the aforementioned algorithms were proven to accurately in real-time with a pie-slice technique. However, the pie-slice classifier is the accuracy-limiting factor in these systems. Although it is possible to address this problem by using a more complex shape for specifying the colour decision region, this would only increase the chances of overfitting. We propose a hybrid colour classification system that automatically searches for the best colour space for classifying any target colour. Moreover, this paper also investigates the general selection of training sets to get a better understanding of the generalisation capability of FCCF-HAGA. The experiments used a professional Munsell ColorChecker Chart with extreme illumination conditions where the colour channels start hitting their dynamic range limits.