Analysis dictionary learning for scene classification
This paper proposes a new framework for scene classification based on an analysis dictionary learning approach. Despite their tremendous success in various image processing tasks, synthesis-based and analysis-based sparse models fall short in classification tasks. It was hypothesized that this is partly due to the linear dependence of the dictionary atoms. In this work, we aim at improving classification performances by compensating for such dependence. The proposed methodology consists in grouping the atoms of the dictionary using clustering methods. This allows to sparsely model images from various scene classes and use such a model for classification. Experimental evidence shows the benefit of such an approach. Finally, we propose a supervised way to train the baseline representation for each class-specific dictionary, and achieve multiple classification by finding the minimum distance between the learned baseline representation and the data's sub-dictionary representation. Experiments seem to indicate that such approach achieves scene-classification performances that are comparable to the state of the art.