Abstract
Consider a single image such that multivariate data are observed at respective pixels. Image classification is a problem of classifying pixels into several homogeneous regions by learning the feature vectors and the adjacency relationships of the pixels in the image. The classification of a pixel into one of categories is an important and fundamental problem in image pattern analysis. In this paper, we review image classification methods, Markov-random-fields (MRF)-based method as well as Spatial Boosting (SpatialBoost) which proposed by Nishii & Eguchi (2005) via statistical machine learning. Variants of SpatialBoost considered in various situations are also discussed. These methods are successfully applied to real and synthetic data, and compared with MRF-based methods.
