An active contour model based on fused texture features for image segmentation
نویسندگان
چکیده
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 nonconvex 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. & 2014 Elsevier B.V. All rights reserved.
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عنوان ژورنال:
- Neurocomputing
دوره 151 شماره
صفحات -
تاریخ انتشار 2015