An Efficient Image Pattern Recognition System Using an Evolutionary Search Strategy

A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which deals with non-uniform images, from the histogram region of interest by thresholds (HROIT). Compared with the performance achieved by support vector machine (SVM) using the whole primitive texture features, the GP-EM methodology, as a whole, achieves a better performance of 90.20% recognition rate on diagnosis, while projecting the hyperspace of the primitive features onto the space of a single generated feature.