Energy minimization via graph cuts for semantic place labeling

This paper presents a novel framework for semantic place labeling by formulating the problem in terms of energy minimization. A method based on graph cuts is used to minimize energy for a function of data cost and smoothness cost. While the data term aims at assigning visual observations to a set of pre-specified place categories, using appearance-based hierarchical classifiers, the smoothness term incorporates contextual evidence from neighbors to ensure that the labels vary smoothly almost everywhere while preserving discontinuities at the borders between adjacent places in the environment. Our proposed method achieved a performance of 91.85%, labeling 2,146 images from the challenging COLD database with place semantics. Correct labeling of 14.5% of images was the result of incorporating contextual information.