Aesthetic quality classification via subject region extraction

• Published in: Proceedings - International Conference on Image Processing pp. 536 - 540
• Main Authors: Jonghee Kim, Changick Kim
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2014
• Subjects: Aesthetic quality classification; Computer vision; Face; Feature extraction; Histograms; Image color analysis; Machine learning; Random forests; Subject region extraction; Vectors; Visualization
• DOI: 10.1109/ICIP.2014.7025107
• ISSN: 1522-4880

Aesthetic quality classification of photos gains growing interest in recent years. In this paper, we propose an aesthetic quality classification method via subject region extraction. We extract the subject region by a combination of clear region detection and saliency detection. Once the subject regions are extracted, we extract regional features to measure contrast between the subject and background regions since people usually emphasize objects by focusing them. Global features are used to describe comprehensive properties of the image. Experimental results show that our classification performance outperforms the state-of-the-art aesthetic quality classification methods even if we do not use prior knowledge of a visual content.