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Article

Image Aesthetic Assessment Based on Image Classification and Region Segmentation

1
GIPSA Lab, Grenoble Alpes University, 11 rue des Mathematiques, Grenoble Campus BP 46, F-38402 Saint Martin d’Heres CEDEX, France
2
Faculty of Information Technology, Vietnam Maritime University, 484 Lach Tray, Le Chan, Hai Phong 04000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 24 November 2020 / Revised: 14 December 2020 / Accepted: 17 December 2020 / Published: 27 December 2020
The main goal of this paper is to study Image Aesthetic Assessment (IAA) indicating images as high or low aesthetic. The main contributions concern three points. Firstly, following the idea that photos in different categories (human, flower, animal, landscape, …) are taken with different photographic rules, image aesthetic should be evaluated in a different way for each image category. Large field images and close-up images are two typical categories of images with opposite photographic rules so we want to investigate the intuition that prior Large field/Close-up Image Classification (LCIC) might improve the performance of IAA. Secondly, when a viewer looks at a photo, some regions receive more attention than other regions. Those regions are defined as Regions Of Interest (ROI) and it might be worthy to identify those regions before IAA. The question “Is it worthy to extract some ROIs before IAA?” is considered by studying Region Of Interest Extraction (ROIE) before investigating IAA based on each feature set (global image features, ROI features and background features). Based on the answers, a new IAA model is proposed. The last point is about a comparison between the efficiency of handcrafted and learned features for the purpose of IAA. View Full-Text
Keywords: image aesthetic assessment; region of interest; sharpness map; color saliency map; large field image; close-up image; image classification; exif; handcrafted features; learned features image aesthetic assessment; region of interest; sharpness map; color saliency map; large field image; close-up image; image classification; exif; handcrafted features; learned features
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MDPI and ACS Style

Le, Q.-T.; Ladret, P.; Nguyen, H.-T.; Caplier, A. Image Aesthetic Assessment Based on Image Classification and Region Segmentation. J. Imaging 2021, 7, 3. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010003

AMA Style

Le Q-T, Ladret P, Nguyen H-T, Caplier A. Image Aesthetic Assessment Based on Image Classification and Region Segmentation. Journal of Imaging. 2021; 7(1):3. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010003

Chicago/Turabian Style

Le, Quyet-Tien; Ladret, Patricia; Nguyen, Huu-Tuan; Caplier, Alice. 2021. "Image Aesthetic Assessment Based on Image Classification and Region Segmentation" J. Imaging 7, no. 1: 3. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010003

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