A Multi-Scale Cascaded Hierarchical Model for Image Labeling

Image labeling is an important and challenging task in the area of graphics and visual computing, where datasets with high quality labeling are critically needed. In this paper, based on the commonly accepted observation that the same semantic object in images with different resolutions may have different representations, we propose a novel multi-scale cascaded hierarchical model (MCHM) to enhance general image labeling methods. Our proposed approach first creates multi-resolution images from the original one to form an image pyramid and labels each image at different scale individually. Next, it constructs a cascaded hierarchical model and a feedback circle between image pyramid and labeling methods. The original image labeling result is used to adjust labeling parameters of those scaled images. Labeling results from the scaled images are then fed back to enhance the original image labeling results. These naturally form a global optimization problem under scale-space condition. We further propose a desirable iterative algorithm in order to run the model. The global convergence of the algorithm is proven through iterative approximation with latent optimization constraints. We have conducted extensive experiments with five widely used labeling methods on five popular image datasets. Experimental results indicate that MCHM improves labeling accuracy of the state-of-the-art image labeling approaches impressively.