Psychophysical Determination of the Relevant Colours That Describe the Colour Palette of Paintings

In an early study, the so-called “relevant colour” in a painting was heuristically introduced as a term to describe the number of colours that would stand out for an observer when just glancing at a painting. The purpose of this study is to analyse how observers determine the relevant colours by describing observers’ subjective impressions of the most representative colours in paintings and to provide a psychophysical backing for a related computational model we proposed in a previous work. This subjective impression is elicited by an efficient and optimal processing of the most representative colour instances in painting images. Our results suggest an average number of 21 subjective colours. This number is in close agreement with the computational number of relevant colours previously obtained and allows a reliable segmentation of colour images using a small number of colours without introducing any colour categorization. In addition, our results are in good agreement with the directions of colour preferences derived from an independent component analysis. We show that independent component analysis of the painting images yields directions of colour preference aligned with the relevant colours of these images. Following on from this analysis, the results suggest that hue colour components are efficiently distributed throughout a discrete number of directions and could be relevant instances to a priori describe the most representative colours that make up the colour palette of paintings.

[1]  Robert Benavente,et al.  Psychophysical Measurements to Model Intercolor Regions of Color-Naming Space , 2009 .

[2]  Fahad Shahbaz Khan,et al.  Beyond Eleven Color Names for Image Understanding , 2018, Machine Vision and Applications.

[3]  D. Field,et al.  EFFICIENT NEURAL CODING OF NATURAL IMAGES , 2008 .

[4]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[5]  Sara S. Patterson,et al.  Reconciling Color Vision Models With Midget Ganglion Cell Receptive Fields , 2019, Front. Neurosci..

[6]  Néstor Parga,et al.  Information processing by a perceptron in an unsupervised learning task , 1993 .

[7]  M. Menu,et al.  Visual perception of natural colours in paintings: An eye‐tracking study of Grünewald's Resurrection , 2021 .

[8]  J R Mureika,et al.  Multifractal structure in nonrepresentational art. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Claudia Mello-Thoms,et al.  Visual interest in pictorial art during an aesthetic experience. , 2007, Spatial vision.

[10]  J. Romero,et al.  Computational color analysis of paintings for different artists of the XVI and XVII centuries , 2018 .

[11]  Javier Romero,et al.  Heuristic analysis influence of saliency in the color diversity of natural images , 2018 .

[12]  Javier Romero,et al.  Computing the relevant colors that describe the color palette of paintings. , 2020, Applied optics.

[13]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[14]  M. Yelizaveta,et al.  Analysis and Retrieval of Paintings Using Artistic Color Concepts , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[15]  Li Zhaoping,et al.  Theoretical understanding of the early visual processes by data compression and data selection , 2006, Network.

[16]  Mateu Sbert,et al.  Categorizing art: Comparing humans and computers , 2009, Comput. Graph..

[17]  K. Gegenfurtner,et al.  Color Perception: Objects, Constancy, and Categories. , 2018, Annual review of vision science.

[18]  S. Nascimento,et al.  The number of discernible colors in natural scenes. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[19]  Peter König,et al.  What's color got to do with it? The influence of color on visual attention in different categories. , 2008, Journal of vision.

[20]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[21]  S. Nascimento,et al.  Statistics of colors in paintings and natural scenes. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[22]  T. W. Lee,et al.  Chromatic structure of natural scenes. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Tsuhan Chen,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < , 2022 .

[24]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[25]  Kenichiro Masaoka,et al.  Number of discernible object colors is a conundrum. , 2013, Journal of the Optical Society of America. A, Optics, image science, and vision.

[26]  C. J. Bartleson.  Memory colors of familiar objects. , 1960, Journal of the Optical Society of America.

[27]  J. Hanley.  Color Categorical Perception , 2015 .

[28]  Christian J. Kellner,et al.  A distributed code for color in natural scenes derived from center-surround filtered cone signals , 2013, Front. Psychol..

[29]  Paul S. Heckbert.  Color image quantization for frame buffer display , 1982, SIGGRAPH.

[30]  Catarina A. R. João,et al.  The colors of paintings and viewers’ preferences , 2017, Vision Research.

[31]  Johan Wagemans,et al.  Order, complexity, and aesthetic appreciation. , 2020 .

[32]  David H Foster,et al.  The Verriest Lecture: Color vision in an uncertain world. , 2018, Journal of the Optical Society of America. A, Optics, image science, and vision.

[33]  Michael Felsberg,et al.  Painting-91: a large scale database for computational painting categorization , 2014, Machine Vision and Applications.

[34]  Berit Brogaard,et al.  Encyclopedia of Color Science and Technology , 2019 .

[35]  Arash Afraz,et al.  Richer color vocabulary is associated with better color memory but not color perception , 2020, Proceedings of the National Academy of Sciences.

[36]  C. Redies,et al.  Statistical regularities in art: Relations with visual coding and perception , 2010, Vision Research.