Multi-feature based Foggy Image Classification

Classifying the density of foggy images is an important step in further processing for foggy images. In this paper, we propose a fog density estimation algorithm from the multifeature perspective. Based on the analysis of the characteristics of foggy images with different levels of density, three features of color, edge gradient and transmittance are adopted to construct the feature-pool. During the process of feature constructing, the type of color space is firstly determined via the linear discriminant analysis (LDA). Then the histograms of the three features are extracted to form the whole feature. Finally, classification is completed by training a support vector machine classifier. Experimental results show that the method can accurately classify the density of foggy images, which has certain theoretical and practical significance.