Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

Bodini, Matteo

doi:10.3390/inventions4030034

Cited by 10 publications

(5 citation statements)

References 90 publications

(160 reference statements)

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“…Due to the small number of images in the two datasets, the proposed approach cannot be compared to deep-learning-based approaches [ 47 , 48 , 49 , 50 , 51 ]. The proposed approach will be compared with three related approaches; the first one is based on the Birkhoff model [ 52 , 53 ], where Shannon entropy and image compressibility are used to represent the order and complexity of the Birkhoff model.…”

Section: Resultsmentioning

confidence: 99%

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Khalili

Bouchachia

2021

Entropy

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The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In this paper, we will study the aesthetic assessment of simple visual patterns. The proposed approach suggests that aesthetically appealing patterns are more likely to deliver a higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is evaluated using two datasets; the results show that the proposed approach is more accurate in classifying aesthetically appealing patterns compared to some related approaches that use different complexity measures.

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Section: Resultsmentioning

confidence: 99%

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Khalili

Bouchachia

2021

Entropy

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“…The work has provided hard evidence of the trend in philosophy that beauty has started to be accepted as an objective concept [ 4 ]. Artificial neural networks have been used to train a massive number of crowdsourced images for assessing their goodness or scenicness [ 48 , 49 , 50 ]. The result developed by Seresinhe and her colleagues on scenicness of areas, although harvested from subjective judgement of individual people, is consistent with our results from the previous section.…”

Section: Implications Of the Computational Approach And Future Workmentioning

confidence: 99%

Structural Beauty: A Structure-Based Computational Approach to Quantifying the Beauty of an Image

Jiang

Rijke

2021

J. Imaging

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To say that beauty is in the eye of the beholder means that beauty is largely subjective so varies from person to person. While the subjectivity view is commonly held, there is also an objectivity view that seeks to measure beauty or aesthetics in some quantitative manners. Christopher Alexander has long discovered that beauty or coherence highly correlates to the number of subsymmetries or substructures and demonstrated that there is a shared notion of beauty—structural beauty—among people and even different peoples, regardless of their faiths, cultures, and ethnicities. This notion of structural beauty arises directly out of living structure or wholeness, a physical and mathematical structure that underlies all space and matter. Based on the concept of living structure, this paper develops an approach for computing the structural beauty or life of an image (L) based on the number of automatically derived substructures (S) and their inherent hierarchy (H). To verify this approach, we conducted a series of case studies applied to eight pairs of images including Leonardo da Vinci’s Mona Lisa and Jackson Pollock’s Blue Poles. We discovered among others that Blue Poles is more structurally beautiful than the Mona Lisa, and traditional buildings are in general more structurally beautiful than their modernist counterparts. This finding implies that goodness of things or images is largely a matter of fact rather than an opinion or personal preference as conventionally conceived. The research on structural beauty has deep implications on many disciplines, where beauty or aesthetics is a major concern such as image understanding and computer vision, architecture and urban design, humanities and arts, neurophysiology, and psychology.

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“…Thus, it can be used in transfer learning approaches. Several times, these deep architectures lead the state-of-the-art results on a wide number of challenging classification and regression problems [53][54][55][56][57].…”

Section: Standard Auto Encodersmentioning

confidence: 99%

Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Bodini

2019

Data

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Interactions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: the experiments show an average improvement of 0.17 , 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem.

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Will the Machine Like Your Image? Automatic Assessment of Beauty in Images with Machine Learning Techniques

Cited by 10 publications

References 90 publications

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

An Information Theory Approach to Aesthetic Assessment of Visual Patterns

Structural Beauty: A Structure-Based Computational Approach to Quantifying the Beauty of an Image

Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

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