Paper: A Novel Color-texture Descriptor Based on Local Histograms for Image Segmentation
In this paper, we propose a novel color-texture image segmentation method based on local histograms.
Starting with clustering-based color quantization, we extract a sufficient number of representative colors. For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained.
After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation.
Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors.
Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries.
Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.
Liu, Yang; Liu, Guangda; Liu, Changying; Sun, Changming. A Novel Color-texture Descriptor Based on Local Histograms for Image Segmentation. IEEE Access. 2019; 7:160683-160695. doi.org/10.1109/ACCESS.2019.2951228
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