Correspondence, Matching and Recognition

Abstract

The association of corresponding content across different visual representations and models is a fundamental task in many areas of computer vision. This special issue contains seven timely papers, all of which are concerned with solving a correspondence challenge, an associated matching or recognition task. The presented topics showcase some of the diversity in current computer vision research; they range widely from text recognition, motion segmentation, and cross-modal matching techniques to invariant descriptor construction, and aesthetic image analysis. Pons-Moll et al. present work, which aims at inferring dense data-to-model correspondences. In their paper “Metric Regression Forests for Correspondence Estimation” (doi:10. 1007/s11263-015-0818-9), they introduce a new decision forest training objective named Metric Space Information Gain (MSIG). They show that their methodology is a principled generalization of the proxy classification objective, which does not require an extrinsic isometric embedding of the model surface in Euclidean space. Backed by extensive experiments, the authors demonstrate that this leads to highly accurate associations, using few training images. Matching structures in cases where no one-to-one correspondences, but only relative pairing information is available is addressed in the paper “Relatively-Paired Space Analysis: