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Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling

View Article: PubMed Central

ABSTRACT

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

No MeSH data available.


The colored cross correlation coefficients matrix.
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Figure 4: The colored cross correlation coefficients matrix.

Mentions: In Figure 3, one color represents each type of features that locate in each dimensionality. We can find that the majority of the feature values compactly locates at the bottom of the space and only a few sparsely scatter among the concentrated feature stripes. One possible conclusion is that the features extracted using the algorithms mentioned in Section Materials and Methods are highly redundant, correlative and there is a quite low diversity of the features. In order to further examine this issue, the cross correlation coefficients of the 106-D features are calculated and illustrated in Figure 4. There are 1370 correlation coefficients that are larger than 0.75 in their absolute values, which accounts for 12.19% in total.


Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling
The colored cross correlation coefficients matrix.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631837&req=5

Figure 4: The colored cross correlation coefficients matrix.
Mentions: In Figure 3, one color represents each type of features that locate in each dimensionality. We can find that the majority of the feature values compactly locates at the bottom of the space and only a few sparsely scatter among the concentrated feature stripes. One possible conclusion is that the features extracted using the algorithms mentioned in Section Materials and Methods are highly redundant, correlative and there is a quite low diversity of the features. In order to further examine this issue, the cross correlation coefficients of the 106-D features are calculated and illustrated in Figure 4. There are 1370 correlation coefficients that are larger than 0.75 in their absolute values, which accounts for 12.19% in total.

View Article: PubMed Central

ABSTRACT

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

No MeSH data available.