Study on Chironomid Larvae Recognition Based on DWT and Improved KNN

作     者：赵晶莹 郭海 孙兴滨 ZHAO Jing-ying;GUO Hai;SUN Xing-bin

作者机构：大连民族学院计算机科学与工程学院辽宁大连116600 哈尔滨工业大学市政环境工程学院黑龙江哈尔滨150001 东北林业大学环境科学系黑龙江哈尔滨150040 

出 版 物：《Agricultural Science & Technology》 (农业科学与技术（英文版）)

年 卷 期：2009年第10卷第4期

页      面：146-149页

学科分类：07[理学] 08[工学] 080203[工学-机械设计及理论] 070104[理学-应用数学] 0802[工学-机械工程] 0701[理学-数学] 

基　　金：Supported by the National Natural Science Foundation of China(50778048)(60803096) the Natural Science Foundation of Hei-longjiang Province(E200812) China Postdoctoral ScienceFoundation Funded Project(20070420882) 

主　　题：Freshwater plankton Chironomid larvae Wavelet decomposition Color features K-Nearest Neighbor （KNN） 

摘      要：A chironomid 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 of 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.