Winder, S., Hua, G. and Brown, M. A., 2009. Picking the best DAISY. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009). IEEE, pp. 178-185.
Local image descriptors that are highly discriminative, computational efficient, and with low storage footprint have long been a dream goal of computer vision research. In this paper, we focus on learning such descriptors, which make use of the DAISY configuration and are simple to compute both sparsely and densely. We develop a new training set of match/non-match image patches which improves on previous work. We test a wide variety of gradient and steerable filter based configurations and optimize over all parameters to obtain low matching errors for the descriptors. We further explore robust normalization, dimension reduction and dynamic range reduction to increase the discriminative power and yet reduce the storage requirement of the learned descriptors. All these enable us to obtain highly efficient local descriptors: e.g, 13.2% error at 13 bytes storage per descriptor, compared with 26.1% error at 128 bytes for SIFT.
|Item Type ||Book Sections|
|Creators||Winder, S., Hua, G. and Brown, M. A.|
|Departments||Faculty of Science > Computer Science|
|Publisher Statement||Brown_cvpr_2009.pdf: © 2009 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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