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.

Complexity perception of texture images

CORCHS, SILVIA ELENA
;
2015-01-01

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.
2015
New Trends in Image Analysis and Processing - ICIAP 2015 Workshops. ICIAP 2015 International Workshops BioFor, CTMR, RHEUMA, ISCA, MADiMa SBMI, and QoEM
9783319232218
ICIAP 2015 International Workshops, BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM 7-8 September
Genoa; Italy
2015
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2127962
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