Categorization of digitized artworks by media with brain programming
| Autor: | Gustavo Olague, Mariana Chan-Ley |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Databases
Factual Computer science Feature extraction Image processing computer.software_genre 01 natural sciences Choice Behavior 010309 optics Set (abstract data type) Machine Learning Optics Cognition 0103 physical sciences Humans Computer Simulation Electrical and Electronic Engineering Engineering (miscellaneous) Artificial neural network business.industry Deep learning Brain Models Theoretical Object (computer science) Atomic and Molecular Physics and Optics Light intensity Categorization Paintings Artificial intelligence business computer Natural language processing |
| Zdroj: | Applied optics. 59(14) |
| ISSN: | 1539-4522 |
| Popis: | This work describes the use of brain programming applied to the categorization problem of art media. The art categorization problem—from the standpoint of materials and techniques used by artists—presents a challenging task and is considered an open research area. Brain programming is a machine learning methodology successfully tested for the problem of object categorization; however, when working with art images, the objects in pictures of the same category may be different from each other regarding image content. Therefore, it is necessary to find the best set of functions that extract specific features to identify patterns among different techniques. In this study, we show a comparison with deep learning to understand the limits and benefits of our approach. We train and validate solutions with the Kaggle database and test the best results with the WikiArt database. The results confirm that brain programming matches or surpasses deep learning in three out of five classes (over 90%) while being close (less than 5%) in the remaining two with significantly simpler programs. |
| Databáze: | OpenAIRE |
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