Fourier Transform based Features for Clean and Polluted Water Image Classification
Proceedings - International Conference on Pattern Recognition
Water image classification is challenging because water images of ocean or river share the same properties with images of polluted water such as fungus, waste and rubbish. In this paper, we present a method for classifying clean and polluted water images. The proposed method explores Fourier transform based features for extracting texture properties of clean and polluted water images. Fourier spectrum of each input image is divided into several sub-regions based on angle and spatial information. For each region over the spectrum, the proposed method extracts mean and variance features using intensity values, which results in a feature matrix. The feature matrix is then passed to an SVM classifier for the classification of clean and polluted water images. Experimental results on classes of clean and polluted water images show that the proposed method is effective. Furthermore, a comparative study with the state-of-the-art method shows that the proposed method outperforms the existing method in terms of classification rate, recall, precision and F-measure.
Wu, Xuerong; Shivakumara, Palaiahnakote; Zhu, Liping; Zhang, Hualu; Shi, Jie; Lu, Tong; Pal, Umapada; and Blumenstein, Michael, "Fourier Transform based Features for Clean and Polluted Water Image Classification" (2018). Conference Articles. 41.