一种基于协方差描述子的场景分类算法

Abstract

Scene classification is a hot topic in computer vision.Under the premise of image segmentation,a novel scene classification algorithm is proposed,which combines pixel location,color characteristics,direction features and local texture features to form the covariance descriptor.To avoid computing tedious distance measure in Riemannian space,the covariance descriptor is converted into sigma-point representation,where scene describing and SVM based training can be completed in Euclidian space.The performance of the novel algorithm is compared with some of classical algorithms using SUN Database.Farther more,the robustness of the algorithm is validated with noise appended data samples.The results show that the proposed algorithm not only has advantages on computation time and classification performance,but also has good robustness to scene noise.