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A variational image segmentation method exploring both intensity means and texture patterns
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DOI：10.1016/j.image.2019.05.002.png)
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Existing image segmentation methods usually consider only intensity or texture features, and thus cannot work well on images with both varying intensity and salient texture patterns. This paper aims to solve this problem by exploring both intensity and texture features for better image segmentation. To this end, we propose a novel variational image segmentation model, which uses constant vectors to represent intensity means in different image subregions and structured dictionaries to encode local texture patterns in images. We develop an effective algorithm to implement the proposed variational model by seeking for intensity means and a specific dictionary for the input image and meanwhile computing the representation coefficients of image patches with respect to the learned dictionary. We derive closed-form solutions for all these components, which result in the high efficiency of our proposed method. Extensive evaluation experiments with comparison to existing methods have been done on both synthetic and real-world images. The results demonstrate the superiority of our proposed method in effectively segmenting images with both unsmooth intensity variations and salient texture patterns.
Keyword:
Image segmentation
Variational model
Intensity means
Texture patterns
Dictionary learning
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