基于入侵性杂草优化算法的图像识别的研究

针对小波不变矩提取的特征向量维数过大的问题，提出一种以类间、类内散布矩阵作为可分离判据的离散入侵性杂草优化算法实现特征向量的选择，利用BP神经网络作为分类器进行图像识别。实验仿真结果表明，与现有特征选择算法相比，改进的离散入侵性杂草优化算法对于图像特征向量的选择时间更短，识别正确率更高，能有效提高分类器的性能。

Research of image recognition based on invasive weed optimization algorithm

Due to the large number of feature values which can be extracted from wavelet moment, a discrete invasive weed optimization algorithm is proposed to select the feature vectors with between-class scatter matrix?and within-class scatter matrix, and finally can recognize images with the help of BP neural network as the classifier. The simulation results show that, compared to the feature selection algorithm, the improved discrete invasive weed optimization algorithm has the shorter selection time of image feature vectors, the higher accuracy, and can effectively improve the performance of the classifier.