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Towards Category-Based Aesthetic Models of Photographs

  • Pere Obrador
  • Michele A. Saad
  • Poonam Suryanarayan
  • Nuria Oliver
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7131)

Abstract

We present a novel data-driven category-based approach to automatically assess the aesthetic appeal of photographs. In order to tackle this problem, a novel set of image segmentation methods based on feature contrast are introduced, such that luminance, sharpness, saliency, color chroma, and a measure of region relevance are computed to generate different image partitions. Image aesthetic features are computed on these regions (e.g. sharpness, colorfulness, and a novel set of light exposure features). In addition, color harmony, image simplicity, and a novel set of image composition features are measured on the overall image. Support Vector Regression models are generated for each of 7 popular image categories: animals, architecture, cityscape, floral, landscape, portraiture and seascapes. These models are analyzed to understand which features have greater influence in each of those categories, and how they perform with respect to a generic state of the art model.

Keywords

Image Analysis Image Aesthetics Regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pere Obrador
    • 1
  • Michele A. Saad
    • 2
  • Poonam Suryanarayan
    • 3
  • Nuria Oliver
    • 1
  1. 1.Telefonica ResearchBarcelonaSpain
  2. 2.University of Texas at AustinAustinUSA
  3. 3.The Pennsylvania State UniversityUSA

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