Brown, M. A., Hua, G. and Winder, S., 2011. Discriminative learning of local image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (1), pp. 43-57.
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.
|Item Type ||Articles|
|Creators||Brown, M. A., Hua, G. and Winder, S.|
|Departments||Faculty of Science > Computer Science|
|Publisher Statement||Brown_IEEE-TPAMI_2011_33_1_43.pdf: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works|
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