Neuro-genetic classifier applied to road detection

Image classification has gained an important role in many applications (landscape planning or assessment, meteorology, biodiversity, etc...). This operation aims to separate different regions of an image having various properties in different classes. In this paper, a back-propagation clustering network is proposed for efficient image classification. Our goal is to be able to determine with accuracy some (ROI) regions of interest in a gray level image. To this end, we introduce a new approach that optimizes network performance and improve its learning using genetic algorithms (GA). GAs are applied to optimize internal parameters of the network structure (weights and bias) through a fitness function. The proposed approach generates classification results with high accuracy and reliability. A comparison study is conducted and proved that the combination between a feed forward neural network and genetic algorithm generates better results than other recent methods in the literature based only on neural network trained with backpropagation algorithm.