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Classification of aesthetic natural scene images using statistical and semantic features

Biswas, Kunal; Shivakumara, Palaiahnakote; Pal, Umapada; Lu, Tong; Blumenstein, Michael; Lladós, Josep


Kunal Biswas

Umapada Pal

Tong Lu

Michael Blumenstein

Josep Lladós


Aesthetic image analysis is essential for improving the performance of multimedia image retrieval systems, especially from a repository of social media and multimedia content stored on mobile devices. This paper presents a novel method for classifying aesthetic natural scene images by studying the naturalness of image content using statistical features, and reading text in the images using semantic features. Unlike existing methods that focus only on image quality with human information, the proposed approach focuses on image features as well as text-based semantic features without human intervention to reduce the gap between subjectivity and objectivity in the classification. The aesthetic classes considered in this work are (i) Very Pleasant, (ii) Pleasant, (iii) Normal and (iv) Unpleasant. The naturalness is represented by features of focus, defocus, perceived brightness, perceived contrast, blurriness and noisiness, while semantics are represented by text recognition, description of the images and labels of images, profile pictures, and banner images. Furthermore, a deep learning model is proposed in a novel way to fuse statistical and semantic features for the classification of aesthetic natural scene images. Experiments on our own dataset and the standard datasets demonstrate that the proposed approach achieves 92.74%, 88.67% and 83.22% average classification rates on our own dataset, AVA dataset and CUHKPQ dataset, respectively. Furthermore, a comparative study of the proposed model with the existing methods shows that the proposed method is effective for the classification of aesthetic social media images.


Biswas, K., Shivakumara, P., Pal, U., Lu, T., Blumenstein, M., & Lladós, J. (2023). Classification of aesthetic natural scene images using statistical and semantic features. Multimedia Tools and Applications, 82, 13507–13532.

Journal Article Type Article
Acceptance Date Sep 12, 2022
Publication Date 2023-04
Deposit Date Feb 2, 2024
Journal Multimedia Tools and Applications
Print ISSN 1380-7501
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 82
Pages 13507–13532