Optimizing the color-to-grayscale conversion for image classification. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Güneş, Ali; Kalkan, Habil; Durmuş, Efkan
• Is Part Of: Signal, image and video processing. Volume 10:Issue 5(2016)
• Page Start: 853
• Page End: 860
• Publication Date: 2016-07
• Subjects: Color-to-grayscale conversion -- Genetic algorithm -- RGB color space -- Classification; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0828-7
• Languages: English
• ISSNs: 1863-1703

Abstract

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.