Object Matching using Low-Rank constraint and its Applications
Feature-based object matching is a fundamental problem in computer vision. In this talk, we present a new first-order object (inlier features) matching technique called ROML (Robust Object Matching using Low-rank constraint). Given a set of images with extracted inlier and outlier features, ROML aims to simultaneously identify the inlier features from each image, and establish their consistent correspondences across the image set. This is a challenging combinatorial problem. To achieve the goal, ROML leverages the underlying data low-rank property to simultaneously optimize a partial permutation matrix (PPM) for each image, and feature correspondences are established by the obtained PPMs. Extensive experiments on rigid/non-rigid object matching, matching instances of a common object category, and common object localization demonstrate ROML’s efficacy for feature-based object matching.
Dr. Kui JIA
Date & Time
23 Jun 2015 (Tuesday) 15:00 - 16:00
E11-1036 (University of Macau)
Department of Computer and Information Science
Kui Jia received the B.Eng., M.Eng, and Ph.D. degrees respectively from Northwestern Polytechnic University, National University of Singapore, and Queen Mary, University of London. He is currently a visiting assistant professor with Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau. His research interests are in computer vision, machine learning, and image processing.