Color-texture image segmentation remains a challenging problem due to extensive color-texture variability. Thus, the limited prior knowledge that is expressed by pairwise constraints can be exploited to guide the segmentation process. We propose a new semisupervised method by combining constrained feature selection and spectral clustering (SC) to perform color-texture image segmentation. The pairwise constraints are used by the constraint feature selection to choose the most relevant features among an available set of color and texture features. For this purpose, an innovative constraint score is developed to evaluate a subset of features at one time. A specific constrained SC algorithm involving the pairwise constraints is then applied to regroup the pixels into clusters. Experimental results on four benchmark datasets show that the proposed constraint score outperforms the main state-of-the-art constraint scores and that our semisupervised segmentation method is competitive compared with supervised, semisupervised, and unsupervised state-of-the-art segmentation methods.

中文翻译：

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使用光谱聚类的半监督彩色纹理图像分割的受约束特征选择

由于广泛的颜色纹理可变性，颜色纹理图像分割仍然是一个具有挑战性的问题。因此，可以利用由成对约束表达的有限先验知识来指导分割过程。通过结合约束特征选择和光谱聚类（SC）进行颜色纹理图像分割，我们提出了一种新的半监督方法。约束特征选择使用成对约束在一组可用的颜色和纹理特征中选择最相关的特征。为此，开发了一种创新的约束评分来一次评估一组特征。然后应用涉及成对约束的特定约束SC算法将像素重新分组为群集。