Unsupervised Color Image Segmentation Based on Local Fractal Dimension
نویسندگان
چکیده
This paper proposes an improved version for the JSEG color image segmentation algorithm, combining the classical JSEG algorithm with a local fractal operator that measures the fractal dimension of each pixel, thus improving the boundary detection. Furthermore, the sensitivity of color variation is enhanced when working with the original color value, instead of quantized color information. Experiments with natural color images of the “The Berkeley Segmentation Dataset and Benchmark” (BSDS) are presented, which show improved results, qualitatively and quantitatively, in comparison with the classical JSEG, the Fractal-only and the Fractal-JSEG methods. Keywordscolor image segmentation; J value segmentation (JSEG); local fractal dimension, differential box-counting (DBC).
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