J4 ›› 2014, Vol. 11 ›› Issue (2): 311-321.doi: 10.1016/S1672-6529(14)60040-8

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Modulating a Local Shape Descriptor through Biologically Inspired Color Feature

Hongwei Zhao1,2, Baoyu Zhou1, Pingping Liu1,2, Tianjiao Zhao1   

  1. 1. School of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun 130012, P. R. China
  • 出版日期:2014-03-30
  • 通讯作者: Pingping Liu E-mail:liupp@jlu.edu.cn

Modulating a Local Shape Descriptor through Biologically Inspired Color Feature

Hongwei Zhao1,2, Baoyu Zhou1, Pingping Liu1,2, Tianjiao Zhao1   

  1. 1. School of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University, Changchun 130012, P. R. China
  • Online:2014-03-30
  • Contact: Pingping Liu E-mail:liupp@jlu.edu.cn

摘要:

This paper presents a biologically inspired local image descriptor that combines color and shape features. Compared with previous descriptors, red-cyan cells associated with L, M, and S cones (L for long, M for medium, and S for short) are used to indicate one of the opponent color channels. Stepping forward from state-of-the-art color feature extraction, we exploit a new approach to compute the color orientation and magnitudes of three opponent color channels, namely, red-green, blue-yellow, and red-cyan, in two-dimensional space. Color orientation is calculated in histograms with magnitude weighting. We linearly concatenate the four-color-opponent-channel histogram and scale-invariant-feature-transform histogram in the final step. We apply our biologically inspired descriptor to describe the local image feature. Quantitative comparisons with state-of-the-art descriptors demonstrate the significant advantages of maintaining invariance to photometric and geometric changes in image matching, particularly in cases, such as illumination variation and image blurring, where more color contrast information is observed.

关键词: local image descriptor, color, opponent color, scale-invariant feature transform, image matching

Abstract:

This paper presents a biologically inspired local image descriptor that combines color and shape features. Compared with previous descriptors, red-cyan cells associated with L, M, and S cones (L for long, M for medium, and S for short) are used to indicate one of the opponent color channels. Stepping forward from state-of-the-art color feature extraction, we exploit a new approach to compute the color orientation and magnitudes of three opponent color channels, namely, red-green, blue-yellow, and red-cyan, in two-dimensional space. Color orientation is calculated in histograms with magnitude weighting. We linearly concatenate the four-color-opponent-channel histogram and scale-invariant-feature-transform histogram in the final step. We apply our biologically inspired descriptor to describe the local image feature. Quantitative comparisons with state-of-the-art descriptors demonstrate the significant advantages of maintaining invariance to photometric and geometric changes in image matching, particularly in cases, such as illumination variation and image blurring, where more color contrast information is observed.

Key words: local image descriptor, color, opponent color, scale-invariant feature transform, image matching