Support vector data description for weed/corn image recognition
2010
Liu, Xien | Li, Mengjun | Sun, Yuanlun | Deng, Xiaoyan
This paper focused on support vector data description (SVDD) technique for the weed/corn recognition based on imbalanced weed/corn image samples. The original images were taken from corn fields under natural lighting and various field backgrounds, and converted into gray level images by a vegetation index: Excess Green minus Excess Red (ExG-ExR). The 2-level wavelet transform was employed to decompose grey image into the approximate component and the detail components. The features of weed/corn images, including the morphological features and the wavelet-based energy features, were extracted. The morphological features were obtained from binary weed/corn images. The various combinations of these features were used as input vector to construct support vector data description models. The experiment results showed that the SVDD model could provide better recognition effect than support vector machine (SVM) and Fisher linear discrimination algorithm (FLDA) based upon the same imbalanced training data set. The two “best” SVDD models were obtained, which achieved a weed/corn recognition rate of 95.59%.
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