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Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results

Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification