The aim of this work is to study image complexity perception of real images. We conducted psycho-physical experiments where observers judged the complexity of different datasets of images on a web-based interface [1]. At the end of the test, observers indicated the main characteristics that guided their judgements. The databases differed in the type of visual stimuli used: images representing real scenes and/or texture patches. For real scenes the most relevant criteria used were quantity of objects, details and colors, while for texture patches they were regularity and understandability. Several criteria are adopted simultaneously, confirming the multidimensional aspect of complexity found in the literature [2]. To process the subjective data we applied z-scores and outlier removal. The mean scores are then correlated with different visual features. We considered features based on spatial, color and frequency properties that can be associated to bottom-up processes. To take into account top-down effects like understandability we included a memorability index [3]. We propose an image complexity measure where the features are linearly combined. The optimal weighting coefficients are those that best fit the subjective data and depend on the type of stimuli considered. Our measure, properly tuned, can predict complexity perception of different kind of images, outperforming the single visual features. From our investigation two aspects of image complexity can be underlined: many different perceptual properties are involved and their relative influence depends on the type of stimuli. These considerations are supported by both our computational proposal and the verbal description analysis. [1] Ciocca G, Corchs S, Gasparini F, Bricolo E, Tebano R. Does color influence image complexity perception? In: Fifth IAPR Computational Color Imaging Workshop vol. 9016 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg; ((2015) ):139–148 [2] Oliva A, Mack ML, Shrestha M. Identifying the Perceptual Dimensions of Visual Complexity of Scenes. In: Proc. 26th Annual Meeting of the Cognitive Science Society ((2004) ):101–106 [3] Isola P, Xiao J, Torralba A, and Oliva A. What makes an image memorable? In IEEE Conference on Computer Vision and Pattern Recognition ((2011) ):145–152

Corchs, S., Ciocca, G., Gasparini, F. (2016). Human perception of image complexity: real scenes versus texture patches. In Abstracts for the Second International Meeting of the Milan Center for Neuroscience (Neuromi): Prediction and Prevention of Dementia: New Hope (Milan, July 6-8, 2016).. Ios Press.

Human perception of image complexity: real scenes versus texture patches

CORCHS, SILVIA ELENA;CIOCCA, GIANLUIGI;GASPARINI, FRANCESCA
2016

Abstract

The aim of this work is to study image complexity perception of real images. We conducted psycho-physical experiments where observers judged the complexity of different datasets of images on a web-based interface [1]. At the end of the test, observers indicated the main characteristics that guided their judgements. The databases differed in the type of visual stimuli used: images representing real scenes and/or texture patches. For real scenes the most relevant criteria used were quantity of objects, details and colors, while for texture patches they were regularity and understandability. Several criteria are adopted simultaneously, confirming the multidimensional aspect of complexity found in the literature [2]. To process the subjective data we applied z-scores and outlier removal. The mean scores are then correlated with different visual features. We considered features based on spatial, color and frequency properties that can be associated to bottom-up processes. To take into account top-down effects like understandability we included a memorability index [3]. We propose an image complexity measure where the features are linearly combined. The optimal weighting coefficients are those that best fit the subjective data and depend on the type of stimuli considered. Our measure, properly tuned, can predict complexity perception of different kind of images, outperforming the single visual features. From our investigation two aspects of image complexity can be underlined: many different perceptual properties are involved and their relative influence depends on the type of stimuli. These considerations are supported by both our computational proposal and the verbal description analysis. [1] Ciocca G, Corchs S, Gasparini F, Bricolo E, Tebano R. Does color influence image complexity perception? In: Fifth IAPR Computational Color Imaging Workshop vol. 9016 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg; ((2015) ):139–148 [2] Oliva A, Mack ML, Shrestha M. Identifying the Perceptual Dimensions of Visual Complexity of Scenes. In: Proc. 26th Annual Meeting of the Cognitive Science Society ((2004) ):101–106 [3] Isola P, Xiao J, Torralba A, and Oliva A. What makes an image memorable? In IEEE Conference on Computer Vision and Pattern Recognition ((2011) ):145–152
No
abstract + poster
Texture images, Human perception, Visual Complexity
English
Second International Meeting of the Milan Center for Neuroscience (Neuromi): Prediction and Prevention of Dementia: New Hope
Corchs, S., Ciocca, G., Gasparini, F. (2016). Human perception of image complexity: real scenes versus texture patches. In Abstracts for the Second International Meeting of the Milan Center for Neuroscience (Neuromi): Prediction and Prevention of Dementia: New Hope (Milan, July 6-8, 2016).. Ios Press.
Corchs, S; Ciocca, G; Gasparini, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/130372
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