基于小波分析及改进KNN的红虫识别研究  

Study on Chironomid Larvae Recognition Based on DWT and Improved KNN

作　　者：赵晶莹[1] 郭海[1] 孙兴滨[2,3] 

机构地区：[1]大连民族学院计算机科学与工程学院,辽宁大连116600 [2]哈尔滨工业大学市政环境工程学院,黑龙江哈尔滨150001 [3]东北林业大学环境科学系,黑龙江哈尔滨150040

出　　处：《安徽农业科学》2009年第29期14191-14193,共3页Journal of Anhui Agricultural Sciences

基　　金：国家自然科学基金(50778048)(60803096);黑龙江省自然科学基金(E200812);中国博士后基金资助项目(2007-0420882)

摘　　要：提出了一种小波分析与改进KNN相结合的红虫图像识别方法。该方法采用多辨识小波分解提取图像的小波能量特征,同时结合生物图像颜色特征构造特征向量,然后选择加权改进KNN分类器进行识别,分类器根据特征与分类相关度确定权重,修改距离函数,有效提高了分类精度。通过对红虫、剑水蚤、猛水蚤样本进行分类试验证明,平均识别准确率达到95.41%,验证了该方法的有效性。A ehironomid larvae images recognition method based on wavelet energy feature and improved KNN is developed. Wavelet decomposition and color information entropy are selected to construct vectors for KNN that is used to classify the images. The distance function is modified according to the weight determined by the correlation degree between feature and class, which effectively improves classification accuracy. The result shows the mean accuracy of classification rate is up to 95.41% for freshwater plankton images, such as chironomid larvae, cyclops and harpacticoida.

关 键 词：淡水浮游生物 红虫 小波分解 颜色特征 K近邻 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]