A Graph-Based Approach for Image Segmentation

We present a novel graph-based approach to image segmentation. The objective is to partition images such that nearby pixels with similar colors or grayscale intensities belong to the same segment. A graph representing an image is derived from the similarity between the pixels and partitioned by a computationally efficient graph clustering method, which identifies representative nodes for each cluster and then expands them to obtain complete clusters of the graph. Experiments with synthetic and natural images are presented. A comparison with the well known graph clustering method of normalized cuts shows that our approach is faster and produces segmentations that are in better agreement with visual assessment on original images.