Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models
This work addresses photo aesthetics assessment for applications like image curation, but it is incremental as it builds on existing models without fine-tuning.
The paper tackled image aesthetics assessment by proposing a training-free method that uses composite features from off-the-shelf deep models, achieving state-of-the-art results on common benchmarks.
Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.