Modeling the Perception of Visual Complexity in Texture Images

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

doi:10.5057/ijae.12.223

Cited by 5 publications

(5 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that the understandability of a texture has been considered to be a major factor affecting visual complexity [24,25]. Understandability represents how easily a participant can comprehend a particular stimulus.…”

Section: Discussionmentioning

confidence: 99%

“…The stimuli and target images used in previous studies of visual complexity perception include abstract patterns [14,21], outline images and hieroglyphs [22], texture images [23][24][25], grayscale images [26], paintings [16,19,20], webpages [27,28], and real-world images [8,13,18]. Thus, in conventional studies of visual complexity perception, the research objects have been limited to two-dimensional (2D) images.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the visual perception of textures can be represented in a lower feature space. Three to five textural features have been commonly used to describe perceptual dimensions of textures [24,25,36,37]. We scanned three textures with different visual complexities from physical objects using photogrammetry.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

2020

View full text Add to dashboard Cite

Visual complexity, as an attribute of images related to human perception, has been widely studied in computer science and psychology. In conventional studies, the research objects have been limited to the traditional two-dimensional (2D) patterns or images. Therefore, if depth information is introduced into this scenario, how will it affect our perception of visual complexity of an image? To answer this question, we developed an experimental virtual reality system that enables control and display of three-dimensional (3D) visual stimuli. In this study, we aimed to investigate the effect of depth information on visual complexity perception by comparing 2D and 3D displays of the same stimuli. We scanned three textures with different characteristics to create the experimental stimuli and recruited 25 participants for the experiment. The results showed that depth information significantly increased the visual complexity perception of the texture images. Moreover, depth information had different degrees of impact on visual complexity for different textures. The higher the maximum depth introduced in the 3D image, the more significant the increase in visual complexity perception. The experimental virtual reality system used in this study also provides a feasible experimental tool for future experiments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

2020

View full text Add to dashboard Cite

show abstract

“…Other studies proposed methods based on the statistics of contrast and spatial frequencies of the images [10], and prediction models that use regression analysis to map the relationship between the visual characteristics of the image with the complexity perception [11]. Moreover, there is evidence that objective measures such as Shannon's entropy can be used to correlate subjective evaluations.…”

Section: Original Articlementioning

confidence: 99%

A Method for Evaluating Food Image Complexity from the Photographer’s Perspective

Rodriguez

Masuko

Yamanaka

2021

IJAE

View full text Add to dashboard Cite

How complex should the food photographs of a restaurant menu be? Studies on image complexity have been conducted using objective and subjective approaches. Nevertheless, neither approach considers the factors that photographers commonly manipulate during image creation. Therefore, this paper proposes a methodology to evaluate the impression of complexity considering these factors. Through interviews, factors like the Camera Angle, Background, Lighting, Light Color, and the Number of Decorative Elements were identified and defined. Based on these definitions, food images were analyzed and tested by participants who assessed their impression of complexity. The results suggested that the amount of decorative element is a factor with a significant effect on the impression of complexity. Furthermore, the correlation between the number of decorative elements and the evaluations suggested that measuring food image complexity according to the decoration in the image is a closer representation of the impression of complexity judged by human subjects.

show abstract

“…The regression-based methods include linear or nonlinear fitting 22 and random forest fitting algorithms 23 , which have the disadvantage of being prone to overfitting when the size of the dataset is small. The classification-based methods include support vector machines (SVMs) 24 , 25 , BP neural networks 21 , and convolutional neural networks (CNNs) 26 , 27 , but these methods are also more demanding on datasets and require manual labeling. Different from the above methods, unsupervised clustering-based methods for image complexity perception have also been proposed 28 .…”

Section: Introductionmentioning

confidence: 99%

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Shi

Guo

Wang³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

To solve the long-tail problem and improve the testing efficiency for autonomous navigation systems of unmanned surface vehicles (USVs), a visual image-based navigation scene complexity perception method is proposed. In this paper, we intend to accurately construct a mathematical model between navigation scene complexity and visual features from the analysis and processing of image textures. First, the typical complex elements are summarized, and the navigation scenes are divided into four levels according to whether they contain these typical elements. Second, the textural features are extracted using the gray level cogeneration matrix (GLCM) and Tamura coarseness, which are applied to construct the feature vectors of the navigation scenes. Furthermore, a novel paired bare bone particle swarm clustering (PBBPSC) method is proposed to classify the levels of complexity, and the exact value of the navigation scene complexity is calculated using the clustering result and an interval mapping method. By comparing different methods on the classical and self-collected datasets, the experimental results show that our proposed complexity perception method can not only better describe the level of complexity of navigation scenes but also obtain more accurate complexity values.

show abstract

Modeling the Perception of Visual Complexity in Texture Images

Cited by 5 publications

References 13 publications

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures

A Method for Evaluating Food Image Complexity from the Photographer’s Perspective

Research on the visual image-based complexity perception method of autonomous navigation scenes for unmanned surface vehicles

Contact Info

Product

Resources

About