Name
Title | ||
|---|---|---|
Transfer learning features for predicting aesthetics through a novel hybrid machine learning method |
Abstract | ||
|---|---|---|
The automatic assessment of the aesthetic value of an image is a task with many applications but really complex and challenging, due to the subjective component of the aesthetics for humans. The computational systems that carry out this task are usually composed of a set of ad hoc metrics proposed by the researchers and a machine learning system. We propose a new approach that fully automates the metrics creation process, its filtering and adjustment without human subjectivity. Thus, it does not depend on the authors’ human aesthetic intuitions. Our proposal is therefore based on the integration of two machine learning algorithms: CNN, which works as a feature extractor, and Correlation by Genetic Search (CGS)—a novel regression method, working as a supervised learning method. CGS is based on the creation of an adjusted linear regression model using Pearson’s correlation as a measure of performance in an evolutionary process. Experiments were conducted on a very well-known aesthetics database called “Photo.net” with more than a million images from over 400,000 users. The comparison of results with other approaches using the same dataset demonstrates that the fusion of CNN transfer learning features with this specific machine learning method has achieved robust and significantly better results than other state-of-the-art methods and hybrid approaches in terms of AUROC (0.93), accuracy (0.93) and Pearson’s correlation value (0.94). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00521-019-04065-4 | Neural Computing and Applications |
Keywords | DocType | Volume |
Convolutional neural networks, Feature extraction, Machine learning, Prediction, Classification, Aesthetics assessment, Hybrid model, Transfer learning | Journal | 32 |
Issue | ISSN | Citations |
10 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrián Carballal | 1 | 3 | 3.08 |
| Carlos Fernandez-Lozano | 2 | 20 | 8.32 |
| Jónathan Heras | 3 | 94 | 23.31 |
| Juan Romero | 4 | 411 | 40.18 |