基于迭代式RELIEF和相关向量机的黄瓜图像识别方法

针对目前采摘机器人在图像处理实时性以及识别准确率方面的要求，提出一种基于迭代式RELIEF(I-RELIEF)的相关向量机的算法.该算法首先通过将获取到的样本像素信息输入到I-RELIEF的模块中，计算出样本信息中各特征在分类过程中的权值，即各特征对类别的影响程度；然后将带权值的样本信息输入到相关向量机的算法模块中进行训练后得到像素分类器；最后用得到的分类器去预测未知像素来对像素进行分类，实现对黄瓜果实图像的分割.实验结果表明，这种图像像素分类器对于温室条件下的黄瓜像素的识别率达到80%以上，误识率不到27%，两者的比值达到3.0以上.

A Method for Cucumber Identification Based on Iterative-RELIEF and Relevance Vector Machine

To satisfy the requirement of real-time processing and identification accuracy, a method based on iterative-RELIEF relevance vector machine was proposed. In this method, information of image samples is brought into the module of iterative-RELIEF algorithm, which exports a weight for every feature. Then, the information of image samples with weights is brought into the training module of relevance vector machine (RVM). As a result, an image classifier is made, which can be used to predict the classes of unknown pixels of a image containing a cucumber. In the experiment, the rate of right identification is up to 80% or more, while the rate of false identification is lower than 27%, and the ratio of the two is up to 3.0 or more.