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The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For large-scale, general image dataset, however, there are the competing requirements, including not making complex prior assumptions about the scene, having fast speed and good segmentation quality. In this paper, a two-stage method for image segmentation is presented that incorporates the main principles of region-based segmentation and cluster-analysis approaches. The first stage extracts many regions by watershed approach, which provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects to produce the final segmentation results by an improved fuzzy c-means technique. The proposed approach gives a good tradeoff between the easy usability, efficiency and segmentation quality. The experimental results demonstrate the effectiveness of the proposed approach.