Semantic image segmentation using region bank

Semantic image segmentation assigns a predefined class label to each pixel. This paper proposes a unified framework by using region bank to solve this task. Images are hierarchically segmented leading to region banks. Local features and high-level descriptors are extracted on each region of the bank. Discriminative classifiers are learned based on the histograms of feature descriptors computed from training region bank (TRB). Optimally merging predicted regions of query region bank (QRB) results in semantic labeling. Each algorithmic module used in our system is detailed, and as the proposed framework is generic, any algorithm which fits corresponding modules can be plugged into the framework. Experiments on the challenging Microsoft Research Cambridge (MSRC 21) dataset show that the proposed approach achieves the state-of-the-art performance.