利用Gabor滤波器与蚁群算法进行纹理分割
Texture Segmentation Based on Gabor Filters and Ant Colony Optimization Algorithm
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摘要: 提出了一种利用蚁群算法抽取最优Gabor纹理特征的纹理图像监督分割方法。首先,随机选取纹理样本进行Gabor滤波器变换。然后在蚁群算法的基础上采取Gabor纹理向量与纹理类别中心的距离和最小的方式选择特征子集。其目的在于从全局的角度确定Gabor滤波器的主频率及方向中心,使得不同纹理之间的频率响应差别最大。最后,利用K均值算法在已降维的特征上进行纹理图像分割。实验结果表明,本文方法在合成纹理图像的分割中有较佳表现。Abstract: We propose a supervised method for texture segmentation using ant colony optimization algorithm(ACO) to extract prominent Gabor features.Firstly,training samples are randomly selected from the texture images and exposed to the Gabor filters transform.Secondly,a feature subset corresponding to each sample can be selected by minimizing the distance summation between Gabor texture vectors and the different texture classes based on ACO.It aims to determine the central frequency and orientation of Gabor filters globally.The objective makes the frequency responses between different textures well separated.Finally,K-means algorithm is chosen to segment the test images based on reduced features.The experiments on two synthetical textured images show the promising results using the proposed method.
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