Enhancing tomato clustering evaluation using color correction with improved linear regression in preprocessing phase

Abstract

Color inconsistency poses many difficulties when capturing the same object using different image capture devices. Color is one of main parts in image preprocessing and therefore color correction is needed to calibrate images in order to produce consistent color values. In this paper, we propose a new color correction method by employing combined linear regression with stepwise model to enhance the quality of tomatoes ripeness clustering. Macbeth ColorChecker is needed as a reference image while a test image to be corrected is captured by an Android smartphone camera. There are 12 color levels to be compared between reference and test image. However, only a number of color levels are selected by k-means clustering. The selected color levels are utilized to build a linear regression algorithm with stepwise model. The result confirms that color correction and color constancy increase the clustering performance by 10% up to 40% for all possible configurations.