Image Semantic Representation for Event Understanding

Different events, such as terrorist acts and natural catastrophes, frequently occur across the world. The availability of images on the internet can help to understand events. However, manually selecting representative (helpful) images from a massive amount of data can be infeasible. Here, we propose an image semantic representation method that helps to understand the discrimination of Representative Images (RI) from Nonrepresentative Images (NRI). Our method, called Event Semantic Space (ESS), generates a low-dimensional image representation by exploiting the semantics of some images with high representativeness and some representative components of the events (e.g., places, objects, and people). Results on three real-world events attest the capability of our method to represent events, outperforming three image descriptors individually in ranking tasks and presenting capability of learning patterns of Representative Images.