The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions
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| Publicado en: | Computational Intelligence and Neuroscience : CIN vol. 2018 (2018) |
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| Otros Autores: | , , |
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John Wiley & Sons, Inc.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1687-5265 | ||
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| 024 | 7 | |a 10.1155/2018/1812980 |2 doi | |
| 035 | |a 2111087710 | ||
| 045 | 2 | |b d20180101 |b d20181231 | |
| 084 | |a 130312 |2 nlm | ||
| 100 | 1 | |a Liu, Jianli |u College of Textiles and Clothing, Jiangnan University, Wuxi 214122, China | |
| 245 | 1 | |a The Power of Visual Texture in Aesthetic Perception: An Exploration of the Predictability of Perceived Aesthetic Emotions | |
| 260 | |b John Wiley & Sons, Inc. |c 2018 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a How to interpret the relationship between the low-level features, such as some statistical characteristics of color and texture, and the high-level aesthetic properties, such as warm or cold, soft or hard, has been a hot research topic of neuroaesthetics. Contrary to the black-box method widely used in the fields of machine learning and pattern recognition, we build a white-box model with the hierarchical feed-forward structure inspired by neurobiological mechanisms underlying the aesthetic perception of visual art. In the experiment, the aesthetic judgments for 8 pairs of aesthetic antonyms are carried out for a set of 151 visual textures. For each visual texture, 106 low-level features are extracted. Then, ten more useful and effective features are selected through neighborhood component analysis to reduce information redundancy and control the complexity of the model. Finally, model building of the beauty appreciation of visual textures using multiple linear or nonlinear regression methods is detailed. Compared with our previous work, a more robust feature selection algorithm, neighborhood component analysis, is used to reduce information redundancy and control computation complexity of the model. Some nonlinear models are also adopted and achieved higher prediction accuracy when compared with the previous linear models. Additionally, the selection strategy of aesthetic antonyms and the selection standards of the core set of them are also explained. This research also suggests that the aesthetic perception and appreciation of visual textures can be predictable based on the computed low-level features. | |
| 653 | |a Feature extraction | ||
| 653 | |a Judgments | ||
| 653 | |a Visual perception | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Brain research | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Structural hierarchy | ||
| 653 | |a Neurosciences | ||
| 653 | |a Machine learning | ||
| 653 | |a Medical imaging | ||
| 653 | |a Emotions | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Visual perception driven algorithms | ||
| 653 | |a Redundancy | ||
| 653 | |a Experiments | ||
| 653 | |a Mathematical models | ||
| 653 | |a Regression analysis | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Complexity | ||
| 653 | |a Neighborhoods | ||
| 653 | |a Texture | ||
| 653 | |a Semantics | ||
| 653 | |a Artificial intelligence | ||
| 700 | 1 | |a Lughofer, Edwin |u Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz, A-4040 Linz, Austria | |
| 700 | 1 | |a Zeng, Xianyi |u Université Lille Nord de France, F-59000 Lille, France; GEMTEX, ENSAIT, F-59056 Roubaix, France | |
| 700 | 1 | |a Li, Zhengxin |u School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China | |
| 773 | 0 | |t Computational Intelligence and Neuroscience : CIN |g vol. 2018 (2018) | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2111087710/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2111087710/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2111087710/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |