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Finding near optimum colour classifiers : genetic algorithm-assisted fuzzy colour contrast fusion using variable colour depth : a thesis presented to the Institute of Information and Mathematical Sciences in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, Auckland, New Zealand
This thesis presents a complete self-calibrating illumination intensity-invariant
colour classification system. We extend a novel fuzzy colour processing tech-
nique called Fuzzy Colour Contrast Fusion (FCCF) by combining it with a Heuristic-
assisted Genetic Algorithm (HAGA) for automatic fine-tuning of colour descriptors.
Furthermore, we have improved FCCF’s efficiency by processing colour channels at
varying colour depths in search for the optimal ones. In line with this, we intro-
duce a reduced colour depth representation of a colour image while maintaining
efficient colour sensitivity that suffices for accurate real-time colour-based object
recognition. We call the algorithm Variable Colour Depth (VCD) and we propose
a technique for building and searching a VCD look-up table (LUT). The first part
of this work investigates the effects of applying fuzzy colour contrast rules to vary-
ing colour depths as we extract the optimal rule combination for any given target
colour exposed under changing illumination intensities. The second part introduces
the HAGA-based parameter-optimisation for automatically constructing accurate
colour classifiers. Our results show that for all cases, the VCD algorithm, combined
with HAGA for parameter optimisation improve colour classification via a pie-slice
colour classifier.For 6 different target colours, the hybrid algorithm was able to
yield 17.63% higher overall accuracy as compared to the pure fuzzy approach. Fur-
thermore, it was able to reduce LUT storage space by 78.06% as compared to the
full-colour depth LUT.