Improved DSP matching with RPCA for dense correspondences

Abstract

The Deformable Spatial Pyramid (DSP) matching method is popular for dense matching of images with different scenes but sharing similar semantic content, which achieves high matching accuracy. However, the warped image generated by DSP is not smooth, which mainly results from the noisy flow field by DSP. We observed the flow field could be decomposed into a low-rank term and a sparse term. Meanwhile, Robust Principle Component Analysis (RPCA) is capable of recovering the low-rank component from an observation with sparse noises. So, in this paper we propose to use RPCA to deal with the non-smoothness in DSP by recovering the low-rank term from the flow field. Experiments on VGG and LMO datasets verify that our approach obtains smoother warped image and gains higher matching accuracy than the DSP.