Gaussian mixture model (GMM) is a flexible tool for image segmentation and image classification. However, one main limitation of GMM is that it doesn't consider spatial information. Some authors introduced global spatial information from neighbor pixels into GMM without taking the image content into account. The technique of saliency map, which is based on the human visual system, enhances the image regions with high perceptive information. In this paper , we propose a new model, which incorporates the image content-based spatial information extracted from saliency map into the conventional GMM. The proposed method has several advantages: it is easy to implement into the Expectation Maximization algorithm for parameters estimation and therefore there is only little impact in computational cost. Experimental results performed on the public Berkeley database show that the proposed method outperforms the state-of-art methods in terms of accuracy and computational time.