Aesthetic quality assessment of images via Supervised Locality Preserving CCA
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2017
Aesthetic quality assessment plays an important role in how people organize large image collections. Many studies on aesthetic quality assessment are based on design of hand-crafted features without considering whether attributes conveyed by images can actually affect image aesthetics. This paper presents an aesthetic quality assessment method which uses new visual features. The proposed method utilizes Supervised Locality Preserving Canonical Correlation Analysis (SLPCCA) to derive the new features which maximize correlation between attributes and visual features. Finally, by applying ridge regression to the SLPCCA-based features, successful aesthetic quality assessment is realized.