IEICE TRANSACTIONS on Information
A Linear Manifold Color Descriptor for Medicine Package Recognition
Summary :
This paper presents a color-based method for medicine package recognition, called a linear manifold color descriptor (LMCD). It describes a color distribution (a set of color pixels) of a color package image as a linear manifold (an affine subspace) in the color space, and recognizes an anonymous package by linear manifold matching. Mainly due to low dimensionality of color spaces, LMCD can provide more compact description and faster computation than description styles based on histogram and dominant-color. This paper also proposes distance-based dissimilarities for linear manifold matching. Specially designed for color distribution matching, the proposed dissimilarities are theoretically appropriate more than J-divergence and canonical angles. Experiments on medicine package recognition validates that LMCD outperforms competitors including MPEG-7 color descriptors in terms of description size, computational cost and recognition rate.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.5 pp.1264-1271
- Publication Date
- 2012/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.1264
- Type of Manuscript
- Special Section PAPER (Special Section on Recent Advances in Multimedia Signal Processing Techniques and Applications)
- Category
- Image Processing
Authors
Keyword
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Kenjiro SUGIMOTO, Koji INOUE, Yoshimitsu KUROKI, Sei-ichiro KAMATA, "A Linear Manifold Color Descriptor for Medicine Package Recognition" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 5, pp. 1264-1271, May 2012, doi: 10.1587/transinf.E95.D.1264.
Abstract: This paper presents a color-based method for medicine package recognition, called a linear manifold color descriptor (LMCD). It describes a color distribution (a set of color pixels) of a color package image as a linear manifold (an affine subspace) in the color space, and recognizes an anonymous package by linear manifold matching. Mainly due to low dimensionality of color spaces, LMCD can provide more compact description and faster computation than description styles based on histogram and dominant-color. This paper also proposes distance-based dissimilarities for linear manifold matching. Specially designed for color distribution matching, the proposed dissimilarities are theoretically appropriate more than J-divergence and canonical angles. Experiments on medicine package recognition validates that LMCD outperforms competitors including MPEG-7 color descriptors in terms of description size, computational cost and recognition rate.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.1264/_p
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@ARTICLE{e95-d_5_1264,
author={Kenjiro SUGIMOTO, Koji INOUE, Yoshimitsu KUROKI, Sei-ichiro KAMATA, },
journal={IEICE TRANSACTIONS on Information},
title={A Linear Manifold Color Descriptor for Medicine Package Recognition},
year={2012},
volume={E95-D},
number={5},
pages={1264-1271},
abstract={This paper presents a color-based method for medicine package recognition, called a linear manifold color descriptor (LMCD). It describes a color distribution (a set of color pixels) of a color package image as a linear manifold (an affine subspace) in the color space, and recognizes an anonymous package by linear manifold matching. Mainly due to low dimensionality of color spaces, LMCD can provide more compact description and faster computation than description styles based on histogram and dominant-color. This paper also proposes distance-based dissimilarities for linear manifold matching. Specially designed for color distribution matching, the proposed dissimilarities are theoretically appropriate more than J-divergence and canonical angles. Experiments on medicine package recognition validates that LMCD outperforms competitors including MPEG-7 color descriptors in terms of description size, computational cost and recognition rate.},
keywords={},
doi={10.1587/transinf.E95.D.1264},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - A Linear Manifold Color Descriptor for Medicine Package Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1264
EP - 1271
AU - Kenjiro SUGIMOTO
AU - Koji INOUE
AU - Yoshimitsu KUROKI
AU - Sei-ichiro KAMATA
PY - 2012
DO - 10.1587/transinf.E95.D.1264
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2012
AB - This paper presents a color-based method for medicine package recognition, called a linear manifold color descriptor (LMCD). It describes a color distribution (a set of color pixels) of a color package image as a linear manifold (an affine subspace) in the color space, and recognizes an anonymous package by linear manifold matching. Mainly due to low dimensionality of color spaces, LMCD can provide more compact description and faster computation than description styles based on histogram and dominant-color. This paper also proposes distance-based dissimilarities for linear manifold matching. Specially designed for color distribution matching, the proposed dissimilarities are theoretically appropriate more than J-divergence and canonical angles. Experiments on medicine package recognition validates that LMCD outperforms competitors including MPEG-7 color descriptors in terms of description size, computational cost and recognition rate.
ER -
English
