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Article

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

by 1,*,†,‡, 1,†,‡, 1,†,‡ and 2,†,‡
1
Department of Cartographic Engineering, Geodesy, and Photogrammetry, Universitat Politècnica de València, 46022 Valencia, Spain
2
Colour Research Group, School of Design, University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Current address: Camino de Vera s/n, Edificio 7i, 46022 Valencia, Spain.
These authors contributed equally to this work.
Received: 17 January 2020 / Revised: 27 January 2020 / Accepted: 4 February 2020 / Published: 5 February 2020
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. View Full-Text
Keywords: archaeology; clustering; colorimetry; data mining; machine learning; rock art documentation archaeology; clustering; colorimetry; data mining; machine learning; rock art documentation
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MDPI and ACS Style

Molada-Tebar, A.; Marqués-Mateu, Á.; Lerma, J.L.; Westland, S. Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation. Remote Sens. 2020, 12, 520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030520

AMA Style

Molada-Tebar A, Marqués-Mateu Á, Lerma JL, Westland S. Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation. Remote Sensing. 2020; 12(3):520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030520

Chicago/Turabian Style

Molada-Tebar, Adolfo, Ángel Marqués-Mateu, José L. Lerma, and Stephen Westland. 2020. "Dominant Color Extraction with K-Means for Camera Characterization in Cultural Heritage Documentation" Remote Sensing 12, no. 3: 520. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030520

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