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This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature , which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We conducted extensive experiments on benchmark image datasets and compared the segmentation results with current proposed systems. The experimental results show that the performance of our system is comparable to, or even outperforms, those state-of-the-art algorithms. This is a promising approach as in this empirical study we used only texture-layout filter responses as feature and a basic setting of genetic algorithm. The framework is simple and can be extended and improved for many learning problems.