An outdoor time scenes simulation scheme based on support vector regression with radial basis function on DCT domain

  • Authors:

  • Chen-Chung Liu;Kai-Wen Chuang

  • Affiliations:

  • Department of Electronic Engineering, National Chin-Yi University of Technology, Taiping, Taichung 411, Taiwan, ROC;Department of Electronic Engineering, National Chin-Yi University of Technology, Taiping, Taichung 411, Taiwan, ROC

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, a novel strategy for forecasting outdoor scenes is introduced. This new approach combines the support vector regression in neural network computation and the discrete cosine transform (DCT). In 1995, Vapnik introduced a neural-network algorithm called support vector machine (SVM). During the recent years, due to SVM's high generalization performance and attractive modeling features, it has received increasing attention in the application of regression estimation - which is called support vector regression (SVR). In SVR, a set of color-block images were transformed by the discrete cosine transformation to be the training data. We also used the radial basis function (RBF) of the training data as 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 were conducted. The experiment results showed that our proposed algorithm can precisely simulate the desired scenes at an assigned time and has two 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 SVR on the spatial pixels of scene images, it simplifies the regression procedure and saves the processing time.