Authors:
Mathias Gudiksen
1
;
Sebastian Falk
1
;
Lasse Nymark Hansen
1
;
Frederik Brønnum Jensen
1
and
Andreas Møgelmose
2
Affiliations:
1
Department of Electronic Systems, Aalborg University, Denmark
;
2
Department of Architecture and Media Technology, Aalborg University, Denmark
Keyword(s):
Exposure Estimation, Aesthetic Evaluation, Handcrafted Features, Neural Network, Deep Learning, Regression.
Abstract:
In recent years, computer vision systems have excelled in detection and classification problems. Many vision tasks, however, are not easily reduced to such a problem. Often, more subjective measures must be taken into account. Such problems have seen significantly less research. In this paper, we tackle the problem of aesthetic evaluation of photographs, particularly with respect to exposure. We propose and compare three methods for estimating the exposure value of a photograph using regression: SVM on handcrafted features, NN using image histograms, and the VGG19 CNN. A dataset containing 844 images with different exposure values was created. The methods were tested on both the full photographs and a cropped version of the dataset. Our methods estimate the exposure value of our test set with an MAE of 0.496 using SVM, an MAE of 0.498 using NN, and an MAE of 0.566 using VGG19, on the cropped dataset. Without a face detector we achieve an MAE of 0.702 for SVM, 0.766 using NN, and 1.56
0 for VGG19. The models based on handcrafted features or histograms both outperform the CNN in the case of simpler scenes, with the histogram outperforming the handcrafted features slightly. However, on more complicated scenes, the CNN shows promise. In most cases, handcrafted features seem to be the better option, despite this, the use of CNNs cannot be ruled out entirely.
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