Affiliation:
1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan Province 410082, P. R. China
Abstract
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.
Funder
National Natural Science Foundation of China
Publisher
World Scientific Pub Co Pte Lt
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献