Spatial Pyramid Convolutional Neural Network for Social Event Detection in Static Image
This work addresses social event detection for applications like photo organization and ads recommendation, but it is incremental as it builds on existing CNN methods with a spatial pyramid configuration.
The paper tackled social event detection in static images by proposing a spatial pyramid configuration for a convolutional neural network classifier, achieving average accuracy improvements of 15% and 2% over baseline methods on two datasets.
Social event detection in a static image is a very challenging problem and it's very useful for internet of things applications including automatic photo organization, ads recommender system, or image captioning. Several publications show that variety of objects, scene, and people can be very ambiguous for the system to decide the event that occurs in the image. We proposed the spatial pyramid configuration of convolutional neural network (CNN) classifier for social event detection in a static image. By applying the spatial pyramid configuration to the CNN classifier, the detail that occurs in the image can observe more accurately by the classifier. USED dataset provided by Ahmad et al. is used to evaluate our proposed method, which consists of two different image sets, EiMM, and SED dataset. As a result, the average accuracy of our system outperforms the baseline method by 15% and 2% respectively.