Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation

Abstract

The camera characterization procedure has been recognized as a convenient methodology to correct color recordings in cultural heritage documentation and preservation tasks. Instead of using a whole color checker as a training sample set, in this paper, we introduce a novel framework named the Patch Adaptive Selection with K-Means (P-ASK) to extract a subset of dominant colors from a digital image and automatically identify their corresponding chips in the color chart used as characterizing colorimetric reference. We tested the methodology on a set of rock art painting images captured with a number of digital cameras. The characterization approach based on the P-ASK framework allows the reduction of the training sample size and a better color adjustment to the chromatic range of the input scene. In addition, the computing time required for model training is less than in the regular approach with all color chips, and obtained average color differences Δ E a b * lower than two CIELAB units. Furthermore, the graphic and numeric results obtained for the characterized images are encouraging and confirms that the P-ASK framework based on the K-means algorithm is suitable for automatic patch selection for camera characterization purposes.

Preliminary Identification of Mixtures of Pigments Using the paletteR Package in R—The Case of Six Paintings by Andreina Rosa (1924–2019) from the International Gallery of Modern Art Ca’ Pesaro, Venice

“…In order to further calibrate the images to obtain a more accurate reproduction, the ColorChecker ® Classic Mini Target (CC) created by X-Rite [17] (see Appendix A, Figure A1) was introduced in all images to act as a colorimetric reference. Since this color chart was manufactured to cover 24 of the most common colors in photographic and industrial applications [18] and the paintings of Andreina Rosa were characterized by a narrow color palette, this color chart was considered enough for the image analysis. However, when a wider range of colors is observed within the examined artworks, the Digital ColorChecker SG [19] color chart is recommended as it consists of 140 color patches.…”

Towards a More Sustainable and Less Invasive Approach for the Investigation of Modern and Contemporary Paintings

“…The ColorChecker ® Classic Mini Target (CC) created by X-Rite (Grand Rapids Michigan, MI, USA) [26] was placed near the artworks during the acquisition of the images to act as a colorimetric reference. This color target includes 24 colors that are generally encountered in photography and industry [27]. The CC used in this research was acquired specifically for this color analysis on images, and it has been stored in its pouch to limit color fading that might have been caused by the exposure to light.…”

Practical RGB-to-XYZ Color Transformation Matrix Estimation under Different Lighting Conditions for Graffiti Documentation

“…D65 represents average daylight and has a correlated color temperature of approximately 6500 K [ 10 ]. Previous investigations in rock painting scenarios indicated that satisfactory results were obtained using second-order polynomial models for colorimetric camera characterization [ 11 , 27 , 28 ]. However, in practice, it is not possible to work under controlled lighting conditions, especially not in outdoor photography where there is rarely a single reference white point present.…”