Discovering affective regions in deep convolutional neural networks for visual sentiment prediction

In this paper, we address the problem of automatically recognizing emotions in still images. While most of current work focus on improving whole-image representations using CNNs, we argue that discovering affective regions and supplementing local features will boost the performance, which is inspired by the observation that both global distributions and salient objects carry massive sentiments. We propose an algorithm to discover affective regions via deep framework, in which we use an off-the-shelf tool to generate N object proposals from a query image and rank these proposals with their objectness scores. Then, each proposal's sentiment score is computed using a pre-trained and fine-tuned CNN model. We combine both scores and select top K regions from the N candidates. These K regions are regarded as the most affective ones of the input image. Finally, we extract deep features from the whole-image and the selected regions, respectively, and sentiment label is predicted. The experiments show that our method is able to detect the affective local regions and achieve state-of-the-art performances on several popular datasets.