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Ground-based cloud classification plays an essential role in meteorological research and has received great concern in recent years. In this paper, a novel algorithm named multiple random projections (MRP) is proposed for ground-based cloud classification. The proposed algorithm uses an ensemble approach of MRP to obtain an optimized textons. Based on the textons, discriminative features can be obtained for classification. A series of experiments on two ground-based cloud databases (Kiel and IapCAS-E) are conducted to evaluate the efficiency of our proposed method. In addition, three current state-of-the-art methods, which include Patch, PCA, single random projection (SRP), are selected for comparison purpose. The experimental results show that our MRP method can achieved the best classification performance.
Date of Conference: 16-18 Dec. 2012