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Authors: Hai-Xia Long ; Li Zhou ; Qiang Zhang ; Jing Zhang and Xiao-Guang Li

Affiliation: Beijing University of Technology, China

Keyword(s): Dual Dimensionality Reduction, Locality Preserving Projections, Sparse Coding, Image Classification.

Abstract: The statistical property of Bag of Word (BoW) model and spatial property of Spatial Pyramid Matching (SPM) are usually used to improve distinguishing ability of features by adding redundant information for image classification. But the increasing of the image feature dimension will cause “curse of dimensionality” problem. To address this issue, a dual dimensionality reduction scheme that combines Locality Preserving Projection (LPP) with the Principal Component Analysis (PCA) has been proposed in the paper. Firstly, LPP has been used to reduce the feature dimensions of each SPM and each dimensionality reduced feature vector is cascaded into a global vector. After that, the dimension of the global vector is reduced by PCA. The experimental results on four standard image classification databases show that, compared with the benchmark ScSPM( Sparse coding based Spatial Pyramid Matching), when the dimension of image features is reduced to only 5% of that of the baseline scheme, the class ification performance of the dual dimensionality reduction scheme proposed in this paper still can be improved about 5%. (More)

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Paper citation in several formats:
Long, H.; Zhou, L.; Zhang, Q.; Zhang, J. and Li, X. (2016). An Efficient Dual Dimensionality Reduction Scheme of Features for Image Classification. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 672-678. DOI: 10.5220/0005787506720678

@conference{visapp16,
author={Hai{-}Xia Long. and Li Zhou. and Qiang Zhang. and Jing Zhang. and Xiao{-}Guang Li.},
title={An Efficient Dual Dimensionality Reduction Scheme of Features for Image Classification},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP},
year={2016},
pages={672-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005787506720678},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP
TI - An Efficient Dual Dimensionality Reduction Scheme of Features for Image Classification
SN - 978-989-758-175-5
IS - 2184-4321
AU - Long, H.
AU - Zhou, L.
AU - Zhang, Q.
AU - Zhang, J.
AU - Li, X.
PY - 2016
SP - 672
EP - 678
DO - 10.5220/0005787506720678
PB - SciTePress

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