Forecasting Outdoor Scenes with Support Vector Regression and the Radial Basis Function
نویسندگان
چکیده
In this paper, a novel strategy for forecasting outdoor scenes is introduced. It is an approach combining the support vector regression in neural network computation, the discrete cosine transform. In 1995, Vapnik first introduced a neural-network algorithm called support vector machine (SVM). During recent years, due to SVM‘s high generalization performance and its attractive modeling features , it have received increasing attention in the application of regression estimation. The regression estimation of SVM called support vector regression (SVR). A set of color-block images were transformed by the discrete cosine transformation, they are used as the training data for SVR. We also use the radial basis function (RBF) of the training data as the SVR’s kernel to establish the RBF neural network. Finally, the time scenes simulation algorithm (TSSA) is able to synthesize the corresponding scene of any assigned time of the original outdoor scene image. To explore the utility and demonstrate the efficiency of the proposed algorithm, simulations under various input images are conducted. The experiment results show that our proposed algorithm can precisely simulate the desired scenes at an assigned time and has three advantages: (A) Using the color-block images instead of using the scene images of a place to create the reference database, the database can be used for any outdoor scene image taken at anywhere at anytime. (B) Taking the support vector regression on the DCT coefficients of scene images instead of taking the multiple regression on the spatial pixels of scene images, it simplifies the support vector regression procedure and save the processing time.
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