Mixture Distributions for Weakly Supervised Classification in Remote Sensing Images
Jean-Baptiste Bordes, V. Prinet · 2008
For its simplicity and efficiency, the bag-of-words representation based on appearance features is widely used in image and text classification. Its drawback is that shape patterns of the image are neglected. This paper presents a novel image classification approach using a bag-of-words representation of textons while taking into account spatial information. A generative probabilistic modeling of the distribution of textons is proposed. The parameters of the mixture’s components are estimated using a EM algorithm. We show that the number of classes in a database can be found automatically and exactly by MDL. This modeling gives very good results for the task of weakly supervised classification in satellite images. 1