A Deep Learning Neural Network for Classifying Good and Bad Photos
| Autor: | Stephen Lou Banal, Vic Ciesielski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Ground truth
Training set Artificial neural network Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Regression analysis 02 engineering and technology Machine learning computer.software_genre Salience (neuroscience) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Artificial Intelligence in Music, Sound, Art and Design ISBN: 9783030438586 EvoMUSART |
| DOI: | 10.1007/978-3-030-43859-3_1 |
| Popis: | Current state-of-the-art solutions that automate the assessment of photo aesthetic quality use deep learning neural networks. Most of these networks are either binary classifiers or regression models that predict the aesthetic quality of photos. In this paper, we developed a deep learning neural network that predicts the opinion score rating distribution of a photo’s aesthetic quality. Our work focused on finding the best pre-processing method for improving the correlation between ground truth and predicted aesthetic rating distribution of photos in the AVA dataset. We investigated three ways of image resizing and two ways of extracting regions based on salience. We found that the best pre-processing method depended on the photos chosen for the training set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|