Visual complexity assessment of painting images

Guo, Xiaoying; Kurita, Takio; Asano, Chie Muraki; Asano, Atsushi

doi:10.1109/icip.2013.6738080

Cited by 19 publications

(17 citation statements)

References 11 publications

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“…In this paper, we attempted to derive a computational model for an important aspect of visual perception: visual complexity. In our previous paper [16], we identified and determined that there are five important characteristics which affect the human visual complexity: regularity, roughness, directionality, density and understandability. All the characteristics have a strong linear relationship with the visual complexity.…”

Section: Discussionmentioning

confidence: 99%

“…3) Mapping these texture characteristics with visual complexity. We have finished the first two steps in our previous papers [16,17]. It was identified that visual complexity of a texture was influenced by five important texture characteristics: regularity, roughness, directionality, density and understandability.…”

Section: Our Workmentioning

confidence: 99%

“…2.1 Texture characteristics that affect visual complexity Visual complexity of a texture image is affected by five texture characteristics including regularity, roughness, directionality, density and understandability, which was identified in our previous paper [16]. Among these characteristics, regularity, roughness, directionality and density reflect the primary features of a texture image, whereas understandability reflects respondents' prior knowledge and experience.…”

Section: The Perception Of Visual Complexity In Texture Imagesmentioning

confidence: 99%

“…Perception of visual complexity in textures is very important for visual understanding and visual aesthetic evaluation. If we are able to measure and combine computational features of a texture to match "visual complexity" as judged by human subjects, then it would be the first step in being able to predict the effect of visual complexity of textures used in the product design and its effect on human visual aesthetic evaluation [2].…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Perception of Visual Complexity in Texture Images

Guo

Asano

et al. 2013

IJAE

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Perception of visual complexity in textures is very important for visual understanding and visual aesthetic evaluation. In this paper, we propose a new model of estimating subjective visual complexity perception of texture images. Compared with the traditional complexity measures based on information theory and fuzzy theory, the proposed model considers human visual perception, and it predicts the visual complexity of a texture corresponding to the subjective visual impression. Multiple linear regression (MLR) is used as a mapping function to map the relationship between the visual complexity perception and five texture characteristics including regularity, roughness, directionality, density and understandability. F-test and correlation analysis are applied to stimulated data and predicted data. The results of F-test (P < 0.01) prove that the proposed model can significantly predict the visual complexity of a texture, and the correlation coefficient between calculated complexity and subjective complexity (r = 0.951) of the testing textures shows that the results predicted by the proposed model are very close to the visual complexity judged by human subjects.

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Section: Discussionmentioning

confidence: 99%

Section: Our Workmentioning

confidence: 99%

Section: The Perception Of Visual Complexity In Texture Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Perception of Visual Complexity in Texture Images

Guo

Asano

et al. 2013

IJAE

Self Cite

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“…Paintings can be segmented into different parts to extract local features and the first and SLSs (the second largest segments) contain important information. Using the software packages and methods, such as MarvinSegment, in Marvin Framework (http://marvinproject.sourceforge.net) to calculate the segmentation attributes [21], we extract the first largest segment (FLS), SLS and calculate the following features [22].…”

Section: Image Segmentsmentioning

confidence: 99%

Visual order of Chinese ink paintings

Fan

Zhang

2020

Vis. Comput. Ind. Biomed. Art

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Visual order is one of the key factors influencing the aesthetic judgment of artworks. This paper reports the results of evaluating the influence of extracted features on visual order in Chinese ink paintings, using a regression model. We use nine contemporary artists’ paintings as examples and extract features related to the visual order of their paintings. A questionnaire survey is conducted to collect people’s rating scores on the visual order. Via regression modeling, our research analyzes the significance of each feature and validates the influences of the features on the visual order.

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Art Image Complexity Measurement Based on Visual Cognition: Evidence from Eye-Tracking Metrics

Weng

Zhang

et al. 2021

Advances in Neuroergonomics and Cognitive Engineering

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Visual complexity assessment of painting images

Cited by 19 publications

References 11 publications

Modeling the Perception of Visual Complexity in Texture Images

Modeling the Perception of Visual Complexity in Texture Images

Visual order of Chinese ink paintings

Art Image Complexity Measurement Based on Visual Cognition: Evidence from Eye-Tracking Metrics

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