一种改进的水果特征提取算法

介绍了颜色矩、Hu矩、Zernike矩、小波矩等特征提取算法，改进了大小特征提取算法。针对单一特征提取算法提取特征信息不全面，不能区别对待识别样本，识别率低等问题提出了一种改进特征提取算法，该算法由上述五种算法通过特征距离自优化组合生成。介绍了算法公式，执行流程，结合项目建立了特征库。通过选取几类易于混淆的水果进行识别试验，结果表明采用改进特征提取算法的识别率明显优于单一特征提取算法，只是识别的平均时间略有延长，但可满足实时识别的要求的别足。

Improved fruit feature extraction algorithm

The feature extraction algorithms such as Color moment, Hu moment, Zernike moment and Wavelet moment are analyzed and a size feature extraction algorithm is improved. According to the problems that a single feature extraction algorithm for picking up the feature information is not comprehensive, and its recognition rate for the samples is very low. An improved feature extraction algorithm which is composed of five algorithms through feature distance from the self-optimizing combination is proposed. The algorithm formula and flow of execution of this new algorithm are introduced, and the feature library of project is established. Selecting some easily confused fruits to do identification experiment, and the results show that using this improvement feature extraction algorithm is superior to single feature extraction algorithm in recognition rate. The average time of recognition is slightly extended, but it can meet the demands of real time identification very well.