PHOG-Derived Aesthetic Measures Applied to Color Photographs of Artworks, Natural Scenes and Objects

Previous research in computational aesthetics has led to the identification of multiple image features that, in combination, can be related to the aesthetic quality of images, such as photographs. Moreover, it has been shown that aesthetic artworks possess specific higher-order statistical properties, such as a scale-invariant Fourier spectrum, that can be linked to coding mechanisms in the human visual system. In the present work, we derive novel measures based on a PHOG representation of images for image properties that have been studied in the context of the aesthetic assessment of images previously. We demonstrate that a large dataset of colored aesthetic paintings of Western provenance is characterized by a specific combination of the PHOG-derived aesthetic measures (high self-similarity, moderate complexity and low anisotropy). In this combination, the artworks differ significantly from seven other datasets of photographs that depict various types of natural and man-made scenes, patterns and objects. To the best of our knowledge, this is the first time that these features have been derived and evaluated on a large dataset of different image categories.

[1]  Joachim Denzler,et al.  1/f2 Characteristics and Isotropy in the Fourier Power Spectra of Visual Art, Cartoons, Comics, Mangas, and Different Categories of Photographs , 2010, PloS one.

[2]  David J Field,et al.  Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. , 2007, Spatial vision.

[3]  Mateu Sbert,et al.  Informational Aesthetics Measures , 2008, IEEE Computer Graphics and Applications.

[4]  Joachim Denzler,et al.  PHOG analysis of self-similarity in aesthetic images , 2012, Electronic Imaging.

[5]  Branka Spehar,et al.  Perceptual and Physiological Responses to Jackson Pollock's Fractals , 2011, Front. Hum. Neurosci..

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  E. Leeuwenberg,et al.  Birkhoff revisited: beauty as a function of effect and means. , 1985, The American journal of psychology.

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

[9]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

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

[11]  Alessandro Verri,et al.  Pattern Recognition with Support Vector Machines , 2002, Lecture Notes in Computer Science.

[12]  Semir Zeki,et al.  Art and the brain , 1998 .

[13]  C. Redies,et al.  Artists portray human faces with the Fourier statistics of complex natural scenes , 2007, Network.

[14]  Zygmunt Pizlo,et al.  Investigation of the role of aesthetics in differentiating between photographs taken by amateur and professional photographers , 2012, Electronic Imaging.

[15]  Richard P Taylor.  Order in Pollock's chaos. , 2002, Scientific American.

[16]  Joachim Denzler,et al.  Fractal-like image statistics in visual art: similarity to natural scenes. , 2007, Spatial vision.

[17]  Yan Ke,et al.  The Design of High-Level Features for Photo Quality Assessment , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[19]  W. Chu.  Studying Aesthetics in Photographic Images Using a Computational Approach , 2013 .

[20]  R. Arnheim.  Art and visual perception: A psychology of the creative eye, New version , 1955 .

[21]  Florian Hönig.  Defining Computational Aesthetics , 2005, CAe.

[22]  Francesca Odone,et al.  Image Kernels , 2002, SVM.

[23]  T. Jacobsen,et al.  Aesthetic Judgments of Novel Graphic Patterns: Analyses of Individual Judgments , 2002, Perceptual and motor skills.

[24]  C. Redies,et al.  A universal model of esthetic perception based on the sensory coding of natural stimuli. , 2007, Spatial vision.