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A new method for boundary detection in natural images is here proposed, consisting of two levels, or twostage sequential processes: embedded integration and post-processing integration. In the embedded integration, two different methods to measure homogeneity in region-growing technique are integrated, based on a global statistical property: the shape of the power spectrum of the image being analyzed. One homogeneity measure is the J value (provided by the classical JSEG algorithm) and the second measure is a multifractal measurement. This first step provides a region extraction. In the second level, edge information is extracted by a classical method, and integrated with region information. This structure, called KSS, eliminates false boundaries in the region map, guided by the edge map, and the noise in edge map as well, now guided by the region map, thus taking the advantage of their complementary nature. Experiments on a large dataset of natural color images show that the result of such two-level strategy matches the human perception better than the individual methods, quantitatively and qualitatively speaking.

TWO-LEVEL STRATEGY FOR IMAGE BOUNDARY DETECTION