Similarity based context for nonparametric scene parsing

Abstract

Scene parsing is an important research area in computer vision which aims to provide semantic label for each pixel in an image. In this paper, we propose a new approach in non-parametric scene parsing. Typical non-parametric scene parsing approaches have two main steps: retrieving similar images to test image and label transferring from retrieved images to the test image. In our approach, in the label transferring step, we use an objective function in which object level and context level information are incorporated. The main contribution of this paper is to propose a new contextual term which it is adapted to the employed similarity distance measure in the retrieval stage. Also, we propose a new adaptive weighting procedure which balances the effectiveness of object-level and context level terms in the objective function. To evaluate the proposed approach, it is applied on the MSRC-21 datasets. The obtained results show that our approach outperforms comparable state-of-the-art nonparametric approaches.