  In this paper, we propose a new deep network that learns multi-level deep
representations for image emotion classification (MldrNet). Image emotion can
be recognized through image semantics, image aesthetics and low-level visual
features from both global and local views. Existing image emotion
classification works using hand-crafted features or deep features mainly focus
on either low-level visual features or semantic-level image representations
without taking all factors into consideration. The proposed MldrNet combines
deep representations of different levels, i.e. image semantics, image
aesthetics, and low-level visual features to effectively classify the emotion
types of different kinds of images, such as abstract paintings and web images.
Extensive experiments on both Internet images and abstract paintings
demonstrate the proposed method outperforms the state-of-the-art methods using
deep features or hand-crafted features. The proposed approach also outperforms
the state-of-the-art methods with at least 6% performance improvement in terms
of overall classification accuracy.
