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Color-to-Grayscale: Does the Method Matter in Image Recognition?

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  • Christopher Kanan
  • Garrison W Cottrell

Abstract

In image recognition it is often assumed the method used to convert color images to grayscale has little impact on recognition performance. We compare thirteen different grayscale algorithms with four types of image descriptors and demonstrate that this assumption is wrong: not all color-to-grayscale algorithms work equally well, even when using descriptors that are robust to changes in illumination. These methods are tested using a modern descriptor-based image recognition framework, on face, object, and texture datasets, with relatively few training instances. We identify a simple method that generally works best for face and object recognition, and two that work well for recognizing textures.

Suggested Citation

  • Christopher Kanan & Garrison W Cottrell, 2012. "Color-to-Grayscale: Does the Method Matter in Image Recognition?," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-7, January.
    • Handle: RePEc:plo:pone00:0029740
    DOI: 10.1371/journal.pone.0029740
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    1. Wang, Jian & Li, Xin & Zhang, Zhenggui & Li, Xiaofei & Han, Yingchun & Feng, Lu & Yang, Beifang & Wang, Guoping & Lei, Yaping & Xiong, Shiwu & Xin, Minghua & Wang, Zhanbiao & Li, Yabing, 2022. "Application of image technology to simulate optimal frequency of automatic collection of volumetric soil water content data," Agricultural Water Management, Elsevier, vol. 269(C).
    2. Fu, J.L. & Qu, Z.G. & Zhang, J.F. & Zhang, G.B., 2023. "Performance analysis of PEMEC with non-uniform deformation based on a comprehensive numerical framework coupling image recognition and CFD," Applied Energy, Elsevier, vol. 350(C).
    3. Katherine L. Hermann & Shridhar R. Singh & Isabelle A. Rosenthal & Dimitrios Pantazis & Bevil R. Conway, 2022. "Temporal dynamics of the neural representation of hue and luminance polarity," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

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