基于小波包分解及模糊支持向量机的红虫识别

Chironomid larvae recognition method based on wavelet packet decomposition and fuzzy support vector machine

水源中的红虫随输送管道进入给水系统,其在给水处理系统中繁殖并进入管网,对水质感官指标造成了重要影响。针对红虫特点提出了一种小波包分解与模糊支持向量机相结合的红虫图像识别方法。该方法采用多层小波包分解提取子图像的能量特征,同时结合生物图像颜色特征构造特征向量,然后选择模糊支持向量机作为分类器进行识别。通过对红虫、猛水蚤、剑水蚤等水厂中主要出现的浮游生物样本进行分类实验证明,该方法能够有效地识别红虫,为水厂的红虫防治提供有效依据。

The chironomid larvaes enter the water-supply system through transmission pipeline. Although there are no indications that chironomid larvae pose a threat to public health, their presence is still not appreciated because most people associate the organisms with low hygiene. In accordance with the characteristics of the organisms, this paper studied chironomid larvae images recognition method based on wavelet packet decomposition and Fuzzy Support Vector Machine （ FSVM）. The energy features of sub-graph decomposed by wavelet packet, color information entropy, and the shape features of plankton were selected to construct feature vector for FSVM that is used to classify the image. The experimental result shows the method is effective for freshwater plankton images, such as chironomid larvae, cyclops and harpacticoida, and provides the basis for the prevention and treatment of chironomid larvaes in water plant.