Aesthetics in digital photography:
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| Format: | Book |
| Language: | English |
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London
ISTE
2023
Hoboken, NJ Wiley |
| Series: | Interdisciplinarity, science and humanities series
|
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxii, 299 Seiten Illustrationen, Diagramme, Karte |
| ISBN: | 9781786307538 |
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| adam_text | Contents Introduction: image and Gaze............................................................. xi Chapter 1. The Legacy of Philosophers.............................................. 1 1.1. The objectivist approach............................................................................ 3 1.1.1. The source: ancient Greece................................................................... 3 1.1.2. After Greece.......................................................................................... 5 1.1.3. Kant and modern aesthetics ............................................................. 7 1.1.4. Objectivism after Kant: from pseudo-subjectivism to aesthetic realism ......................................................................................... 9 1.2. The subjectivist approach........................................................................... 13 1.2.1. From classicism to romanticism....................................................... 14 1.2.2. The moderns...................................................................................... 15 1.2.3. The influence of neurobiology............................................................. 18 1.3. Subjectivism and objectivism: an ongoing debate................................ 19 Chapter 2. Neurobiology or the Arbitrator of Consciousness .... 25 2.1. fMRI protocols and neuroaesthetics....................................................... 2.2. The fMRI quest for “beauty processes” in the brain............................. 2.2.1. The role of the prefrontal
cortex....................................................... 2.2.2. The role of the insular cortex............................................................. 2.2.3. The role of the visual areas............................................................... 2.2.4. The role of memory and cognition.................................................... 2.2.5. The role of embodiment .................................................................. 2.3. Responses from functional electric encephalography .......................... 2.4. A global cognitive scheme for aesthetic judgment?............................. 2.4.1. J. Petitot’s neurogeometric model.................................................... 2.4.2. A. Chatterjee’s aesthetic emotion model ......................................... 2.4.3. The model by Brown ct al................................................................... 27 28 28 31 33 35 35 36 39 40 40 42
vi Aesthetics in Digital Photography 2.4.4. Mode] proposed by H. Leder............................................................. 2.4.5. The model by C. Redies ................................................................... 2.4.6. The emotions model developed by S. Koelsch et al........................... 2.4.7. L.H. Hsu’s model of emotions based on A. Damasio..................... 2.4.8. Other models....................................................................................... 2.5. A critique of neuroaesthetic methods.................................................... 2.5.1. Criticism of neuroaesthetic methods................................................. 2.5.2. Criticisms of the objectives of neuroaesthetics................................ Chapter 3. What Are the Criteria For a Beautiful Photo? 43 45 47 47 50 51 51 52 ................ 55 3.1. Before we enter into the fray................................................................... 3.1.1. What reference books do we have?................................................. 3.1.2. “Beauty of an image” or “quality of an image”?............................. 3.1.3. A glossary of aesthetic appraisal....................................................... 3.1.4. Measuring beauty.............................................................................. 3.2. Composition............................................................................................ 3.2.1. Complexity versus simplicity............................................................. 3.2.2.
Unity.................................................................................................. 3.2.3. A specific case in composition: landscapes...................................... 3.2.4. Using oculometry to analyze composition...................................... 3.2.5. Format or aspect ratio......................................................................... 3.2.6. The rule of thirds (RoT)...................................................................... 3.2.7. The center of the image...................................................................... 3.2.8. Other rules for composition ............................................................. 3.3. Histograms, spectral properties and textures......................................... 3.3.1. Histograms and gray levels................................................................ 3.3.2. Focus, spectral density, fractals ....................................................... 3.3.3. Textures............................................................................................... 3.4. Color........................................................................................................ 3.4.1. About the concept of color................................................................ 3.4.2. Preferences related to isolatedcolors.............................................. 3.4.3. Preferences related to color palettes................................................. 3.5. What behavioral psychosociology has to say......................................... 3.5.1. Images of nature
.............................................................................. 3.5.2. The aesthetics of faces ..................................................................... 3.5.3. The role of the signature, title and context...................................... 3.5.4. Perception and memory: prototypicality......................................... 56 56 57 58 60 63 63 64 64 67 68 70 72 73 76 76 78 80 82 82 84 86 93 93 96 99 101 Chapter 4. Algorithmic Approaches to “Calculate” Beauty............. 103 4.1. First steps: C. Henry................................................................................. 4.2. G.D. Birkhoff ’s mathematical approach................................................. 4.3. Those who followed G.D. Birkhoff....................................................... 4.3.1. Beauty according to H.J. Eysenck.................................................... 103 104 106 106
Contents vii 4.3.2. The Post-War years: the designers, A. Moles and M. Bense .... 4.3.3. A dynamic approach: P. Machado and A. Cardoso.......................... 4.3.4. Work carried out by J. Rigau, Μ. Feixas and Μ. Bert.................... 4.4. Algorithmic approach with AI: J.Schmidhuber.................................... 106 107 108 110 Chapter 5. The Holy Grail of the Digital World: Artificial Intelligence ........................................................................................... 113 5.1. Which artificial intelligence?.................................................................. 5.1.1. The principles................................................................................... 5.1.2. Learning algorithms.......................................................................... 5.2. Why artificial intelligence in aesthetics? .............................................. 5.3. Expert opinions ...................................................................................... 5.4. The database........................................................................................... 5.4.1. Generalist databases, used for aesthetic judgments ....................... 5.4.2. Databases that are specialized for aesthetic photography............... 5.4.3. Databases dedicated to artistic judgment........................................ 5.4.4. Other image databases that are sometimes used............................. 5.4.5. Increasing databases.......................................................................... 114 114 115 116 118 120 122 126 129
130 131 Chapter 6. Primitive-based Classification Methods......................... 133 6.1. Judging aesthetics................................................................................... 6.1.1. Multimedia primitives: the ACQUINE system (Datta et al.) .... 6.1.2. Edges and chromatic distance: Ke et al............................................. 6.1.3. Photography rules: Luo and Tang and Mavridaki and Mezaris . . . 6.1.4. High-level primitives: Dhar et al........................................................ 6.1.5. Generic descriptors of vision: Marchesotti ct al................................ 6.2. Help in composing beautiful photos...................................................... 6.2.1. The library of aesthetic primitives developed by Su et al.................. 6.2.2. The OSCAR system by Yao et al....................................................... 6.2.3. Embedded systems: Lo el al. and Wang et al.................................... 6.3. Some specific research related to the evaluation of aesthetics using primitives....................................................................................................... 6.3.1. Color harmony: Lu et al..................................................................... 6.3.2. Group photography: Wang et al......................................................... 6.3.3. Social networks and crowdsourcing: Schifanclla et al...................... 6.3.4. Looking at comments: San Pedro et al.............................................. 136 136 137 140 143 144 148 148 148 150 Chapter 7. Deep Neural Network
Systems......................................... 155 7.1. DNNs dedicated to aesthetic evaluation................................................. 7.1.1. High and low resolutions: the RAPID system, Lu et al.................... 7.1.2. The multi-path DMA-Net architecture: Lu et al................................ 7.1.3. Adapting to the size of the image: Mai et al...................................... 157 157 160 160 151 151 153 153 154
viii Aesthetics in Digital Photography 7.1.4. Finding beauty on the Web: Redi et al................................................ 7.1.5. Siamese and GAN networks: Kong et al. and Deng et al.................. 7.1.6. Paying attention to the image construction: A-Lamp..................... 7.2. Variants around the basic DNN architecture ......................................... 7.2.1. Comparing photos between themselves: Schwarz et al..................... 7.2.2. Making use of knowledge of the subject: Kao et al........................... 7.2.3. BDN: halfway between classification and DNN............................. 7.2.4. Using the distribution of the evaluations ......................................... 7.2.5. Extracting a “dramatic” image from a panorama: the Creatism system .................................................................................... 7.3. Written appraisals: analyzing them and formulating new ones............ 7.3.1. Photo critique captioning dataset (PCCD) ...................................... 7.3.2. Neural aesthetic image retriever (NAIR)......................................... 7.3.3. Semantic processing by Ghosal et al.................................................. 7.3.4. Aesthetic multi attribute network (AMAN)...................................... 7.4. Measuring subjective beauty................................................................... 7.4.1. Recommendation systems ................................................................ 7.4.2. Defining the user’s psychological profile......................................... 7.4.3.
Learning the user’s tastes through tests........................................... 7.4.4. Multiplying concurrent expertise....................................................... 163 165 167 170 170 172 174 175 178 179 181 182 182 183 185 186 188 191 194 Chapter 8. A Critical Analysis of Machine Learning Techniques ............................................................................................ 197 8.1. The popularity of studies on aesthetics ................................................. 8.2. A summary of learning methods............................................................. 8.2.1. Which architecture? Which software?.............................................. 8.2.2. What performances? ........................................................................ 8.3. Questioning the hypotheses...................................................................... 8.4. Specific features of beautiful images detected by a computer............... 8.4.1. Some observations on the photos in the AVA database .................. 8.4.2. The scores in the AVA database....................................................... 197 199 199 200 203 204 205 207 ............................................................................................ 213 Appendix 1. A Brief Review of Aesthetics ......................................... 219 Appendix 2. Aesthetics in China 237 Conclusion ........................................................
Contents Appendix 3. The Aesthetic of Persian Miniatures ............................ ix 251 Appendix 4. Aesthetics in Japan ........................................................ 263 References ........................................................................................... 271 ..................................................................................................... 295 Index
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| adam_txt |
Contents Introduction: image and Gaze. xi Chapter 1. The Legacy of Philosophers. 1 1.1. The objectivist approach. 3 1.1.1. The source: ancient Greece. 3 1.1.2. After Greece. 5 1.1.3. Kant and modern aesthetics . 7 1.1.4. Objectivism after Kant: from pseudo-subjectivism to aesthetic realism . 9 1.2. The subjectivist approach. 13 1.2.1. From classicism to romanticism. 14 1.2.2. The moderns. 15 1.2.3. The influence of neurobiology. 18 1.3. Subjectivism and objectivism: an ongoing debate. 19 Chapter 2. Neurobiology or the Arbitrator of Consciousness . 25 2.1. fMRI protocols and neuroaesthetics. 2.2. The fMRI quest for “beauty processes” in the brain. 2.2.1. The role of the prefrontal
cortex. 2.2.2. The role of the insular cortex. 2.2.3. The role of the visual areas. 2.2.4. The role of memory and cognition. 2.2.5. The role of embodiment . 2.3. Responses from functional electric encephalography . 2.4. A global cognitive scheme for aesthetic judgment?. 2.4.1. J. Petitot’s neurogeometric model. 2.4.2. A. Chatterjee’s aesthetic emotion model . 2.4.3. The model by Brown ct al. 27 28 28 31 33 35 35 36 39 40 40 42
vi Aesthetics in Digital Photography 2.4.4. Mode] proposed by H. Leder. 2.4.5. The model by C. Redies . 2.4.6. The emotions model developed by S. Koelsch et al. 2.4.7. L.H. Hsu’s model of emotions based on A. Damasio. 2.4.8. Other models. 2.5. A critique of neuroaesthetic methods. 2.5.1. Criticism of neuroaesthetic methods. 2.5.2. Criticisms of the objectives of neuroaesthetics. Chapter 3. What Are the Criteria For a Beautiful Photo? 43 45 47 47 50 51 51 52 . 55 3.1. Before we enter into the fray. 3.1.1. What reference books do we have?. 3.1.2. “Beauty of an image” or “quality of an image”?. 3.1.3. A glossary of aesthetic appraisal. 3.1.4. Measuring beauty. 3.2. Composition. 3.2.1. Complexity versus simplicity. 3.2.2.
Unity. 3.2.3. A specific case in composition: landscapes. 3.2.4. Using oculometry to analyze composition. 3.2.5. Format or aspect ratio. 3.2.6. The rule of thirds (RoT). 3.2.7. The center of the image. 3.2.8. Other rules for composition . 3.3. Histograms, spectral properties and textures. 3.3.1. Histograms and gray levels. 3.3.2. Focus, spectral density, fractals . 3.3.3. Textures. 3.4. Color. 3.4.1. About the concept of color. 3.4.2. Preferences related to isolatedcolors. 3.4.3. Preferences related to color palettes. 3.5. What behavioral psychosociology has to say. 3.5.1. Images of nature
. 3.5.2. The aesthetics of faces . 3.5.3. The role of the signature, title and context. 3.5.4. Perception and memory: prototypicality. 56 56 57 58 60 63 63 64 64 67 68 70 72 73 76 76 78 80 82 82 84 86 93 93 96 99 101 Chapter 4. Algorithmic Approaches to “Calculate” Beauty. 103 4.1. First steps: C. Henry. 4.2. G.D. Birkhoff ’s mathematical approach. 4.3. Those who followed G.D. Birkhoff. 4.3.1. Beauty according to H.J. Eysenck. 103 104 106 106
Contents vii 4.3.2. The Post-War years: the designers, A. Moles and M. Bense . 4.3.3. A dynamic approach: P. Machado and A. Cardoso. 4.3.4. Work carried out by J. Rigau, Μ. Feixas and Μ. Bert. 4.4. Algorithmic approach with AI: J.Schmidhuber. 106 107 108 110 Chapter 5. The Holy Grail of the Digital World: Artificial Intelligence . 113 5.1. Which artificial intelligence?. 5.1.1. The principles. 5.1.2. Learning algorithms. 5.2. Why artificial intelligence in aesthetics? . 5.3. Expert opinions . 5.4. The database. 5.4.1. Generalist databases, used for aesthetic judgments . 5.4.2. Databases that are specialized for aesthetic photography. 5.4.3. Databases dedicated to artistic judgment. 5.4.4. Other image databases that are sometimes used. 5.4.5. Increasing databases. 114 114 115 116 118 120 122 126 129
130 131 Chapter 6. Primitive-based Classification Methods. 133 6.1. Judging aesthetics. 6.1.1. Multimedia primitives: the ACQUINE system (Datta et al.) . 6.1.2. Edges and chromatic distance: Ke et al. 6.1.3. Photography rules: Luo and Tang and Mavridaki and Mezaris . . . 6.1.4. High-level primitives: Dhar et al. 6.1.5. Generic descriptors of vision: Marchesotti ct al. 6.2. Help in composing beautiful photos. 6.2.1. The library of aesthetic primitives developed by Su et al. 6.2.2. The OSCAR system by Yao et al. 6.2.3. Embedded systems: Lo el al. and Wang et al. 6.3. Some specific research related to the evaluation of aesthetics using primitives. 6.3.1. Color harmony: Lu et al. 6.3.2. Group photography: Wang et al. 6.3.3. Social networks and crowdsourcing: Schifanclla et al. 6.3.4. Looking at comments: San Pedro et al. 136 136 137 140 143 144 148 148 148 150 Chapter 7. Deep Neural Network
Systems. 155 7.1. DNNs dedicated to aesthetic evaluation. 7.1.1. High and low resolutions: the RAPID system, Lu et al. 7.1.2. The multi-path DMA-Net architecture: Lu et al. 7.1.3. Adapting to the size of the image: Mai et al. 157 157 160 160 151 151 153 153 154
viii Aesthetics in Digital Photography 7.1.4. Finding beauty on the Web: Redi et al. 7.1.5. Siamese and GAN networks: Kong et al. and Deng et al. 7.1.6. Paying attention to the image construction: A-Lamp. 7.2. Variants around the basic DNN architecture . 7.2.1. Comparing photos between themselves: Schwarz et al. 7.2.2. Making use of knowledge of the subject: Kao et al. 7.2.3. BDN: halfway between classification and DNN. 7.2.4. Using the distribution of the evaluations . 7.2.5. Extracting a “dramatic” image from a panorama: the Creatism system . 7.3. Written appraisals: analyzing them and formulating new ones. 7.3.1. Photo critique captioning dataset (PCCD) . 7.3.2. Neural aesthetic image retriever (NAIR). 7.3.3. Semantic processing by Ghosal et al. 7.3.4. Aesthetic multi attribute network (AMAN). 7.4. Measuring subjective beauty. 7.4.1. Recommendation systems . 7.4.2. Defining the user’s psychological profile. 7.4.3.
Learning the user’s tastes through tests. 7.4.4. Multiplying concurrent expertise. 163 165 167 170 170 172 174 175 178 179 181 182 182 183 185 186 188 191 194 Chapter 8. A Critical Analysis of Machine Learning Techniques . 197 8.1. The popularity of studies on aesthetics . 8.2. A summary of learning methods. 8.2.1. Which architecture? Which software?. 8.2.2. What performances? . 8.3. Questioning the hypotheses. 8.4. Specific features of beautiful images detected by a computer. 8.4.1. Some observations on the photos in the AVA database . 8.4.2. The scores in the AVA database. 197 199 199 200 203 204 205 207 . 213 Appendix 1. A Brief Review of Aesthetics . 219 Appendix 2. Aesthetics in China 237 Conclusion .
Contents Appendix 3. The Aesthetic of Persian Miniatures . ix 251 Appendix 4. Aesthetics in Japan . 263 References . 271 . 295 Index |
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| spelling | Maître, Henri 1948- Verfasser (DE-588)1288812221 aut Aesthetics in digital photography Henri Maître London ISTE 2023 Hoboken, NJ Wiley xxii, 299 Seiten Illustrationen, Diagramme, Karte txt rdacontent n rdamedia nc rdacarrier Interdisciplinarity, science and humanities series Erscheint auch als Online-Ausgabe 978-1-394-22595-8 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034574417&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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