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In this work, a modular statistical optimization method enriched by the introduction of VQ method dedicated to obtain the effectiveness and the optimal comuting time in image recognition system is poposed. In this aim, a comparative study of two RBF and an SVM classifiers are carried out. For that, features extraction is made based on used image database. These features are gathered into blocks. The statistical validation results allow thus via the suggested optimization loop to test the precision level of each block and to stop when this precision level is optimal. In the majority of the cases, this iterative step allows the computing time reduction of the recognition system. Finally, the introduction of vector quantization method allows more global accuracy to our architecture.

Modular Statistical Optimization and VQ Method for Image Recognition