纹理影像特征选择及K-means聚类优化方法

Texture image feature selection and optimization by using K-means clustering

Gabor变换和K-means算法是最为常用的纹理分析方法。然而,采用Gabor变换得到的纹理特征向量具有较高的维数,影响算法的运行效率;K-means算法也易受初始类中心的影响而导致分类精度下降。因此,通过Relief算法对采用Gabor变换所提取的纹理特征进行选择,得到合适的纹理特征子集。进一步采用差分进化算法,对K-means算法的聚类中心进行优化从而提高纹理识别精度和效率。实验结果表明:提出的方法所需用到的纹理特征向量的维数相对于原始特征集有大幅降低,较之基本的K-means算法,纹理识别的精度也有较明显的提高。

Gabor transform and K-means algorithm are two commonly used texture analysis methods.However,the texture feature vector has a high dimension by using Gabor transform,which will influence the operating efficiency.Meanwhile,K-means algorithm is affected by the initial clustering centers,and it may lead to the decrease of classification accuracy.Although,some optimization algorithms like genetic algorithm and particle swarm optimization algorithm could improve the performance of K-means algorithm to some extent,the optimization effect is difficult to guarantee as the increase of dimension.Hence,the Relief algorithm was applied to make a feature selection for Gabor texture feature,and to obtain a suitable texture feature subset.Furthermore,a differential evolution algorithm was used to optimize the clustering center of K-means algorithm,and enhance the accuracy and efficiency of texture recognition.Experimental results demonstrate that the dimension of texture feature vector by using the proposed method is obviously lower than that by using the original feature set,and the recognition accuracy is also apparently improved than the basic K-means algorithm.