Outdoor shadow estimating using multiclass geometric decomposition based on BLS

Illumination is a significant component of an image, and illumination estimation of an outdoor scene from given images is still challenging yet it has wide applications. Most of the traditional illumination estimating methods require prior knowledge or fixed objects within the scene, which makes them often limited by the scene of a given image. We propose an optimization approach that integrates the multiclass cues of the image(s) [a main input image and optional auxiliary input image(s)]. First, Sun visibility is estimated by the efficient broad learning system. And then for the scene with visible Sun, we classify the information in the image by the proposed classification algorithm, which combines the geometric information and shadow information to make the most of the information. And we apply a respective algorithm for every class to estimate the illumination parameters. Finally, our approach integrates all of the estimating results by the Markov random field. We make full use of the cues in the given image instead of an extra requirement for the scene, and the qualitative results are presented and show that our approach outperformed other methods with similar conditions.