An active contour model based on fused texture features for image segmentation

作者：Wu, Qinggang^*; Gan, Yong; Lin, Bin; Zhang, Qiuwen; Chang, Huawen

来源：Neurocomputing, 2015, 151: 1133-1141.

DOI：10.1016/j.neucom.2014.04.085

• Active contour model (ACM)
• Gray level co-occurrence matrix (GLCM)
• Gabor filter
• Texture fusion
• Texture segmentation
• Dual formulation

Texture image segmentation plays an important role in various computer vision tasks. In this paper, a convex texture image segmentation model is proposed. First, the texture features of Gabor and GLCM (gray level co-occurrence matrix) are extracted for original image. Then, the two kinds of texture features are fused together to effectively construct a discriminative feature space by concatenating with each other. In the image segmentation step, a convex energy function is defined by taking the non-convex vector-valued model of Active Contour without Edges (ACWE) into a global minimization framework (GMAC). The proposed global minimization energy function with fused textures (GMFT) can avoid the existence of local minima in the minimization of the vector-valued ACWE model. In addition, a fast dual formulation is adopted to achieve the efficient contour evolution. The experimental results on synthetic and natural animal images demonstrate that the proposed GMFT model obtains more satisfactory segmentation results compared to two state-of-the-art methods in terms of segmentation accuracy and efficiency.

• 出版日期2015-3-3
• 单位大连海事大学; 郑州轻工业大学