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A novel approach for segmentation of images has been proposed by incorporating the advantages of the mean shift segmentation and the normalized cut partitioning methods. The proposed method preprocesses an image by using the mean shift algorithm to form segmented regions, region nodes are applied to form the weight matrix W instead of these regions, the Ncut method is then introduced for region nodes clustering. Since the number of the segmented region nodes is much smaller than that of the image pixels. The proposed algorithm allows a low-dimensional image clustering with significant reduction of the computational complexity comparing to conventional Ncut method based on direct image pixels. The experimental results also verify that the proposed algorithm behaves an improved performance comparing to the mean shift and the Ncut algorithm.