Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
This work addresses the challenge of processing crisis-related social media imagery for humanitarian organizations, but it appears incremental as it builds on existing deep learning and perceptual hashing techniques.
The paper tackles the problem of filtering redundant and irrelevant images from social media during crises by presenting an image processing pipeline for de-duplication and relevancy filtering, achieving results that demonstrate its significance for optimizing human and machine resource utilization.
The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.