Exploring Object-Centric and Scene-Centric CNN Features and Their Complementarity for Human Rights Violations Recognition in Images

Identifying potential abuses of human rights through imagery is a novel and challenging task in the field of computer vision, which will enable to expose human rights violations over large-scale data that may otherwise be impossible. While standard databases for object and scene categorization contain hundreds of different classes, the largest available dataset of human rights violations contains only four classes. Here, we introduce the human rights archive (HRA) database, a verified-by-experts repository of 3050 human rights violations photographs, labeled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present. With the HRA dataset and a two-phase transfer learning scheme, we fine-tuned the state-of-the-art deep convolutional neural networks (CNNs) to provide human rights violations classification CNNs. We also present extensive experiments refined to evaluate how well object-centric and scene-centric CNN features can be combined for the task of recognizing human rights abuses. With this, we show that the HRA database poses a challenge at a higher level for the well-studied representation learning methods and provide a benchmark in the task of human rights violations recognition in visual context. We expect that this dataset can help to open up new horizons on creating systems that are able to recognize rich information about human rights violations.