Unsupervised color texture segmentation based on multi-scale region-level Markov random field models
Автор: Song Xu, Wu Liang, Liu Guoying
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений, распознавание образов
Статья в выпуске: 2 т.43, 2019 года.
Бесплатный доступ
In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.
Region-level markov random field model, color texture image, image segmentation, wavelet transformation, multiscale
Короткий адрес: https://sciup.org/140243288
IDR: 140243288 | DOI: 10.18287/2412-6179-2019-43-2-264-269
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