Affective image retrieval via multi-graph learning

Images can convey rich emotions to viewers. Recent research on image emotion analysis mainly focused on affective image classification, trying to find features that can classify emotions better. We concentrate on affective image retrieval and investigate the performance of different features on different kinds of images in a multi-graph learning framework. Firstly, we extract commonly used features of different levels for each image. Generic features and features derived from elements-of-art are extracted as low-level features. Attributes and interpretable principles-of-art based features are viewed as mid-level features, while semantic concepts described by adjective noun pairs and facial expressions are extracted as high-level features. Secondly, we construct single graph for each kind of feature to test the retrieval performance. Finally, we combine the multiple graphs together in a regularization framework to learn the optimized weights of each graph to efficiently explore the complementation of different features. Extensive experiments are conducted on five datasets and the results demonstrate the effectiveness of the proposed method.