Automatic Image Segmentation by Positioning a Seed
We present a method that automatically partitions a single image into non-overlapping regions coherent in texture and colour. An assumption that each textured or coloured region can be represented by a small template, called the seed, is used. Positioning of the seed across the input image gives many possible sub-segmentations of the image having same texture and colour property as the pixels behind the seed. A probability map constructed during the sub-segmentations helps to assign each pixel to just one most probable region and produce the final pyramid representing various detailed segmentations at each level. Each sub-segmentation is obtained as the min-cut/max-flow in the graph built from the image and the seed. One segment may consist of several isolated parts. Compared to other methods our approach does not need a learning process or a priori information about the textures in the image. Performance of the method is evaluated on images from the Berkeley database.
KeywordsImage Segmentation Seed Position Automatic Image Segmentation Combine Boundary Human Segmentation
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