基于改进蚁群算法的棉花异性纤维目标特征选择方法

为提高基于机器视觉的棉花异性纤维在线分类的精度和速度，提出一种基于改进蚁群算法的棉花异性纤维图像目标特征选择方法。采用初始选择概率预处理方案，设置特征初始概率，降低了冗余特征影响，缩短了算法搜索时间；利用分段变异运算及取优舍劣策略，对棉花异性纤维的颜色、纹理、形状3类特征进行分段变异，避免了算法局部收敛，选出了全局最优特征集。实验结果表明，改进的蚁群算法比基本蚁群算法优化能力更强，搜索时间更短，优化得到的棉花异性纤维特征子集的特征个数比原特征集减少了2/3，分类正确率由84%提高到93%。

Feature Selection for Cotton Foreign Fiber Objects Based on Improved Ant Colony Algorithm

An optimal feature subset selection method based on improved ant colony algorithm was presented. The initial probability of the feature was related to the ability of classification of the separate feature, which was advantageous to reduce the redundancy and the hunting zone of the optimized algorithm at the same time. Section variation of the feature set avoided local convergence. Experimental results indicated that the proposed algorithm further reduced the search time, got a smaller subset of the optimal feature set of cotton fibers and better classification performance. The classification accuracy rate increased from 84% to 93%.