Feature compression of storedgrain insects based onkernel Fisher discrimination analysis
1.Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education ＆ Jiangsu Province, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.Institute of Electric Power, North China University of Water Conservancy and Hydroelectric Power, Zhengzhou, Henan 450011, China
Abstract：Due to the characteristics of stored-grain insects with multi-species and high similarity among various species，a insects feature compression method was proposed based on kernel Fisher discrimination analysis (KFDA). According to Gaussian RBF kernel function, ten selected morphological digital insect features were analyzed by KFDA. The sample data were mapped from input space to high dimensional feature space through a nonlinear mapping function to extract. nonlinear features of raw space by Fisher discrimination analysis (FDA). According to classifier recognition ratio，KFDA was compared with FDA, principle component analysis (PCA) and kernel principle component analysis (KPCA) . Based on the first four features from KFDA，nine species of storedgrain insects in graindepot were automatically recognized by the nearest neighbor classifier with correct identification ratio of 93.33% for validation set. The results show that KFDA is sensitive to nonlinear features of insects. The feature dimensions can be effectively reduced with high separability among species by KFDA.
2012, Vol. 33 
