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Author
M.A. Tahir
J. Kittler
K. Mikolajczyk
F. Yan
K.E.A. van de Sande
T. Gevers
Year
2009
Title
Visual category recognition using Spectral Regression and Kernel Discriminant Analysis
Event
ICCV Workshop on Subspace Methods (Subspace 2009), Kyoto, Japan
Book/source title
12th International Conference on Computer Vision workshops, ICCV workshops
Pages (from-to)
178-185
Publisher
Piscataway, NJ: IEEE
ISBN
9781424444427
Document type
Conference contribution
Faculty
Faculty of Science (FNWI)
Institute
Informatics Institute (IVI)
Abstract
Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been successful in many classification problems. In this paper, we adopt this solution to VCR and demonstrate its advantages over existing methods both in terms of speed and accuracy. The distinctiveness of this method is assessed experimentally using an image and a video benchmark: the PASCAL VOC Challenge 08 and the Mediamill Challenge. From the experimental results, it can be derived that SR-KDA consistently yields significant performance gains when compared with the state-of-the art methods. The other strong point of using SR-KDA is that the time complexity scales linearly with respect to the number of concepts and the main computational complexity is independent of the number of categories.
URL
go to publisher's site
Language
English
Note
TahirICCVSM2009
Permalink
http://hdl.handle.net/11245/1.314742

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