As an attempt to achieve realistic image segmentation, an efficient segmentation algorithm is proposed. The proposed method aims to represent homogeneous visual objects with few regions while preserving semantic contents of an image as good as possible. This strategy is valid, since homogeneous visual objects occupy most parts of the entire image domain in a typical 'head and shoulder' video sequence and the raggedness within them is much more objectionable than in complex visual objects. For this objective, we adopt a bottom-up approach by using spatial domain information only. For precise initial image segmentation, an efficient marker extraction algorithm utilizing marker clusters is employed. And, an ordered and classified region-merging algorithm is suggested and applied to reduce the number of redundant regions within visual objects. Finally, we eliminate redundant small regions heuristically, according to their topological locations. The experimental results show the realistic segmentation of an image with a marginal number of regions. Particularly, homogeneous visual objects are represented with a few regions. Thus, the proposed method is highly applicable to high-level computer vision problems as well as object-based video coders.