Discovering Class-Specific Spatial Layouts for Scene Recognition
Scene image is a spatial composition of objects and background contexts and finding discriminative spatial layouts is critical for scene recognition. In this letter, we propose an $\ell _1$ -regularized max-margin formulation to discover class-specific spatial layouts by jointly learning the image classifier and the class-specific spatial layouts for scene recognition. Unlike previous methods that classify images into different categories either without considering the spatial layouts explicitly or only using class-generic spatial layout, our proposed method can discover a sparse combination of class-specific spatial layouts for different scenes and boost the recognition performance. Experiments on scene-15, landuse-21, and MIT indoor-67 datasets validate the advantages of our proposed algorithm.