  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.
