Image data field for homogeneous region based segmentation

Some innovative methods for image segmentation inspired by physical world are presented in recent years. Aiming to find homogeneous regions and latent semantic information, the paper presents a novel image segmentation method based on image data field. Image data field, developed by simulating the short-range nuclear forces field theory in the physical world, can effectively represent the spatial interactions of neighborhood pixels. Then, the homogeneous regions are characterized by maximum tolerance classes, which induced by homogeneous attraction relation comparing the contributions of potential values in image data field. More specifically, the proposed method mainly focuses on the images with uneven lighting conditions. Compared with the existing relative methods on a variety of images, the experimental results suggest that the presented method is efficient and effective.

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► Image data field method for homogeneous region based segmentation.
► Image data field effectively represents the spatial interactions of neighborhood pixels.
► Homogeneous attraction relation comparing the contributions of potential values induces maximum tolerance classes.
► A stack searching for different homogeneous regions.
► Image data field-based method is efficient and effective.