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A biologically inspired spatio-chromatic feature for color object recognition

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Abstract

Color information has been acknowledged for its important role in object recognition and scene classification. How to describe the color characteristics and extract combined spatial and chromatic feature is a challenging task in computer vision. In this paper we extend the robust SIFT feature on processed opponent color channels to obtain a spatio-chromatic descriptor for color object recognition. The color information processing is implemented under a biologically inspired hierarchical framework, where cone cells, single-opponent and double-opponent cells are simulated respectively to mimic the color perception of primate visual system. The biologically inspired method is tested for object recognition task on two public datasets, and the results support the potential of our proposed approach.

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Notes

  1. Soccer team dataset is available from: http://lear.inrialpes.fr/people/vandeweijer/data.

  2. Bird dataset is available from: http://www-cvr.ai.uiuc.edu/ponce_grp/data/.

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Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the Provincial Natural Science Foundation of Hubei under Grant 2016CFB278, and the National Natural Science Foundation of China under Grant 61601416.

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Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Geo-Information Processing, College of Computer Science, China University of Geosciences, No. 388 Lumo Road, Wuhan, 430074, China

    Tian Tian & Weijing Song

  2. Department of Information, Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

    Yun Zhang

  3. Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China

    Kim-Kwang Raymond Choo

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  1. Tian Tian
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  2. Yun Zhang
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  3. Kim-Kwang Raymond Choo
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  4. Weijing Song
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Correspondence to Weijing Song.

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Tian, T., Zhang, Y., Choo, KK.R. et al. A biologically inspired spatio-chromatic feature for color object recognition. Multimed Tools Appl 76, 18731–18747 (2017). https://doi.org/10.1007/s11042-016-4252-y

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