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Learning midlevel image features for natural scene and texture classification

Le Borgne, Hervé and Guérin-Dugué, Anne and O'Connor, Noel E. (2007) Learning midlevel image features for natural scene and texture classification. IEEE Transactions on Circuits and Systems for Video Technology, 17 (3). pp. 286-297. ISSN 1051-8215

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Abstract

This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimension

Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:feature extraction; filtering theory; image classification; image coding; image texture; independent component analysis; natural scenes;
Subjects:Computer Science > Information retrieval
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Publisher:Institute of Electrical and Electronics Engineers
Official URL:http://dx.doi.org/10.1109/TCSVT.2007.890635
Copyright Information:Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Circuits and Systems for Video Technology. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Dublin City University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Funders:European Commission FP6-001765
ID Code:252
Deposited On:07 Mar 2008 by DORAS Administrator. Last Modified 05 May 2010 12:36

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