Research on the recognition of chironomid larvae based on SVM
Jingying Zhao, Hai Jiao Guo, Xingbin Sun · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
The traditional method of detecting Chironomid larvaes and plankton in water mostly is observation by Naked Eye, which is inefficient and inaccurate. This paper puts forward the Chironomid larvae image recognition method which is based on the support vector machines and multi-layered wavelet decomposition. Gradation histogram balance strengthening treatment is carried out for the image, so as to improve the contrast ratio and make for the threshold division. For each image, a 36 dimension feature vector is computed via two-level discrete Wavelet transform (DWT). The last step of the proposed approach consists is using support vector machine(SVM) as classifer and Wavelet energy as features to recognize the images. Extensive classification experiments on our image data validate that it is promising to employ the proposed texture features to recognize Chironomid larvaes in image.