Object Matching using Low-Rank constraint and its Applications
Speaker:Dr. Kui JIA
Department of Electrical and Computer Engineering
University of Macau
Date & Time:23 Jun 2015 (Tuesday) 15:00 - 16:00
Venue:E11-1036 (University of Macau)
Organized by:Department of Computer and Information Science

Abstract

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.

Biography

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.