title: Discriminative learning of local image descriptors.
abstract: In this paper, we explore methods for learning local image descriptors from training data. We describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both linear and nonlinear transforms with dimensionality reduction, and make use of discriminant learning techniques such as Linear Discriminant Analysis (LDA) and Powell minimization to solve for the parameters. Using these techniques, we obtain descriptors that exceed state-of-the-art performance with low dimensionality. In addition to new experiments and recommendations for descriptor learning, we are also making available a new and realistic ground truth data set based on multiview stereo data.
authors.affiliation: Computer Vision Laboratory, Ecole Polytechnique Fédérale de Lausanne, EPFL IC-CVLAB, BC 309, Station 14, 1015 Lausanne, Switzerland. matthew.brown@epfl.ch
authors.forename: Matthew
authors.initials: M
authors.lastname: Brown
authors.forename: Gang
authors.initials: G
authors.lastname: Hua
authors.forename: Simon
authors.initials: S
authors.lastname: Winder
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016002:Discriminant Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D017711:Nonlinear Dynamics