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Optimizing the color-to-grayscale conversion for image classification

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Abstract

In many of the computer vision applications, color-to-grayscale conversion algorithms are required to preserve the salient features of the color images, such as brightness, contrast and structure of the color image. The traditional color-to-grayscale conversion algorithms such as National Television Standards Committee (NTSC) may produce mediocre images for visual observation. However, these NTSC grayscale images are not tailored for classification purposes because the objective of NTSC is not to obtain discriminative images. For image classification problems, we present a novel color-to-grayscale conversion method based on genetic algorithm (GA). By using the GA, the color image conversion coefficients are optimized to generate more discriminative grayscale images to decrease the error in image classification problems. In order to analyze the effectiveness of the proposed method, all experimental results are compared with traditional NTSC, equal and Karhunen–Loeve-based color-to-grayscale optimization methods. It is observed that the proposed method converges to more discriminative grayscale images as compared to traditional methods.

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Acknowledgments

This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK, Grant No. 110E238).

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Authors and Affiliations

  1. Vision-Image Processing and Pattern Recognition Laboratory, Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Ali Güneş, Habil Kalkan & Efkan Durmuş

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  1. Ali Güneş
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  2. Habil Kalkan
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  3. Efkan Durmuş
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Correspondence to Ali Güneş.

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Güneş, A., Kalkan, H. & Durmuş, E. Optimizing the color-to-grayscale conversion for image classification. SIViP 10, 853–860 (2016). https://doi.org/10.1007/s11760-015-0828-7

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  • DOI: https://doi.org/10.1007/s11760-015-0828-7

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