Predicting and grouping digitized paintings by style using unsupervised feature learning
Bibliographic Data
| ID | 4688264 |
|---|---|
| Authors | Eren Gultepe (0009-0000-3885-5782, Ontario Tech University, corresponding author), Thomas Edward Conturo, Masoud Makrehchi (0000-0003-0287-9568, Ontario Tech University) |
| Year | 2018 |
| Volume | 31 |
| Pages | 13-23 |
| Publication date | 2018-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Cultural Heritage (JOURNAL) |
| Journal identifiers | ISSN: 1296-2074 • E-ISSN: 1778-3674 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.culher.2017.11.008 |
| PMID | 30034259 |
| OpenAlex | W2777073510 |
| Language | EN |
| Citations received | 6 |
| References cited | 21 |
Art · Linguistics · Painting · Visual arts · Aesthetic Perception and Analysis · Art History and Market Analysis · Computer Science · Cultural Heritage Materials Analysis · Artificial Intelligence
Development model of Iasee in basic painting art learning, based on glass box
Computer simulation of archaeological drawings
Machine Learning for Cultural Heritage
Automation for the artisanal economy
Capturing experts’ knowledge in heritage planning enhanced by AI
Learning feature representation of Iberian ceramics with automatic classification models
Deep learning in neural networks
Reducing the Dimensionality of Data with Neural Networks
ImageNet classification with deep convolutional neural networks
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Backpropagation Applied to Handwritten Zip Code Recognition
Methods for 3D digitization of Cultural Heritage
| Citations per year | 0.86 |
|---|---|
| Citation span | 2019 - 2024 (6) |
| Highly cited | No |