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Complexity Perception of Texture Images

  • Gianluigi Ciocca
  • Silvia Corchs
  • Francesca Gasparini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Visual complexity perception plays an important role in the fields of both psychology and computer vision: it can be useful not only to investigate human perception but also to better understand the properties of the objects being perceived. In this paper we investigate the complexity perception of texture images. To this end we perform a psycho-physical experiment on real texture patches. The complexity of each image is assessed on a continuous scale. At the end of the evaluation, each observer indicates the criteria used to assess texture complexity. The most frequent criteria used are regularity, understandability, familiarity and edge density. As candidate complexity measures we consider thirteen image features and we correlate each of them with the subjective scores collected during the experiment. The performance of these correlations are evaluated in terms of Pearson correlation coefficients. The four measures that show the highest correlations are energy, edge density, compression ratio and a visual clutter measure, in accordance with the verbal descriptions collected by the questionnaire.

Keywords

Image complexity Psycho-physical experiment Color image features Texture 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gianluigi Ciocca
    • 1
    • 2
  • Silvia Corchs
    • 1
    • 2
  • Francesca Gasparini
    • 1
    • 2
  1. 1.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversity of Milano-BicoccaMilanoItaly
  2. 2.NeuroMi - Milan Center for NeuroscienceMilanItaly

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