Color distribution matching using a weighted subspace descriptor

Kenjiro Sugimoto; Sei Ichiro Kamata

doi:10.1109/ICIP.2011.6115783

Color distribution matching using a weighted subspace descriptor

Kenjiro Sugimoto^*, Sei Ichiro Kamata

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents a low-level color descriptor which describes the color distribution of a color image as a weighted subspace in the color space, namely eigenvectors and eigenvalues of the distribution. Thanks to low-dimensionality of color space, the proposed descriptor can provide compact description and fast computation. Furthermore, specialized for color distribution matching, it is more efficient than mutual subspace method (MSM). Experiments on medicine package recognition validate that the proposed descriptor outperforms MSM and MPEG-7 low-level color descriptors in terms of description size, computational cost and recognition rate.

Original language	English
Title of host publication	ICIP 2011
Subtitle of host publication	2011 18th IEEE International Conference on Image Processing
Pages	1697-1700
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2011.6115783
Publication status	Published - 2011 Dec 1
Event	2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: 2011 Sept 11 → 2011 Sept 14

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/Territory	Belgium
City	Brussels
Period	11/9/11 → 11/9/14

Keywords

Low-level color descriptor
medicine package recognition
mutual sub-space method

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2011.6115783

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Sugimoto, K & Kamata, SI 2011, Color distribution matching using a weighted subspace descriptor. in ICIP 2011: 2011 18th IEEE International Conference on Image Processing., 6115783, Proceedings - International Conference on Image Processing, ICIP, pp. 1697-1700, 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, 11/9/11. https://doi.org/10.1109/ICIP.2011.6115783

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abstract = "This paper presents a low-level color descriptor which describes the color distribution of a color image as a weighted subspace in the color space, namely eigenvectors and eigenvalues of the distribution. Thanks to low-dimensionality of color space, the proposed descriptor can provide compact description and fast computation. Furthermore, specialized for color distribution matching, it is more efficient than mutual subspace method (MSM). Experiments on medicine package recognition validate that the proposed descriptor outperforms MSM and MPEG-7 low-level color descriptors in terms of description size, computational cost and recognition rate.",

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AB - This paper presents a low-level color descriptor which describes the color distribution of a color image as a weighted subspace in the color space, namely eigenvectors and eigenvalues of the distribution. Thanks to low-dimensionality of color space, the proposed descriptor can provide compact description and fast computation. Furthermore, specialized for color distribution matching, it is more efficient than mutual subspace method (MSM). Experiments on medicine package recognition validate that the proposed descriptor outperforms MSM and MPEG-7 low-level color descriptors in terms of description size, computational cost and recognition rate.

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BT - ICIP 2011

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Color distribution matching using a weighted subspace descriptor

Abstract

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Keywords

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