Computer Analysis of Pablo Picasso's Artistic Style

Computer Analysis of Pablo Picasso's Artistic Style

Amanda Burcoff (Lawrence Technological University, Southfield, MI, USA) and Lior Shamir (Lawrence Technological University, Southfield, MI, USA)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/IJACDT.2017010101
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In the past few years, computational methods have been becoming increasingly more prevalent in art history. Such methods can reveal new knowledge about art, and provide a novel approach to the studying of art history based on quantitative evidence. Here we used computational analysis to study the artistic style of Pablo Picasso and its change throughout time. Experimental results show a strong correlation between the visual content and the time of painting, evidenced by the ability of the computer to estimate the time of creation by analyzing the visual content of the painting. It also showed that some of the paintings were estimated by the algorithm to be artistically related to a different time period than the time period in which they were painted. The most significant numerical image content descriptor for estimating the time of creation is the fractal structure of the painting, which changed during his career in a nearly linear fashion. The software used in the experiment is publicly available, as well as detailed instructions of using the software.
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The union of computational science and art history has brought a new era in both the research of art history and image analysis, providing novel discoveries concerning the subtle patterns within art and the objective lens through which it can be viewed (Doing et al., 2015). These recent advances have provided evidence that computerized image analysis can be effective in classifying and describing quantitative similarities in the work of different artists (Hurtut, 2011; Shamir & Tarakhovsky, 2012; van den Herik & Postma, 2000). Approaches to automatic analysis of visual art include searching and data organization methods such as the application of query by image content (QBIC) tools to retrieve images in art databases (Holt & Hartwick, 1994), as well as analyzing semantic links between paintings and captions (Tsai, 2007), allowing automatic assignment of captions to paintings the computer has not ‘seen’ before (Barnard et al., 2001).

Another application of computational methodology to art is the detection of fraudulent pieces of artwork (Kroner & Lattner, 1998; Taylor et al., 2007, 1999; Mureika et al., 2004; Lyu et al., 2004; Li et al., 2012), continued by the automatic identification of quantitative visual features that make the artistic style of the painter unique (Berezhnoy et al., 2007; Shamir, 2015).

Substantial research efforts have been invested in automatic classification of paintings. Computers have demonstrated ability to classify paintings by their creating artist (Kammerer et al., 2007; Keren, 2002; Shamir et al., 2010b; Johnson Jr et al., 2008; Shen, 2009; Cetinic & Grgic, 2013), emotion (Zhang et al., 2011, 2013), artistic movement (Zujovic et al., 2009; Culjak et al., 2011; Condorovici et al., 2013), or by keyˇ words associated with the paintings (Barnard et al., 2001; Lewis et al., 2004; Vrochidis et al., 2008). In addition to classification, computational methods can also estimate the similarities between different paintings (Garces et al., 2014), or similarities between painters by analyzing the visual content of their art (Shamir et al., 2010b; Shamir & Tarakhovsky, 2012; Shamir, 2012b; Kim & Kim, 2013; Wang & Takatsuka, 2012). Additional studies that revealed similarities in art detectable by computer analysis include algorithmic identification of the type of drawing tools and materials used to create a piece of art (Lake et al., 2004; Kammerer et al., 2007; Roussopoulos et al., 2010). These computational tools contribute a new perspective to the analysis of art that is less subject to human biases, and uses visual features that can be difficult to sense and quantify without using computer methods.

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