Gathering training sample automatically for social event visual modeling

In recent years, the emergence of social media on the Internet has derived many of interesting research and applications. In this paper, a novel framework is proposed to model the visual appearance of social events using automatically collected training samples on the basis of photo context analysis. While collecting positive samples can be achieved easily thanks to explicitly identifying tags, finding representative negative samples from the vast amount of irrelevant multimedia documents is a more challenging task. Here, we argue and demonstrate that the most common negative sample, originating from the same location as the event to be modeled, are best suited for the task. A novel ranking approach is devised to select a set of negative samples. The visual event models are learned from automatically collected samples using SVM. The results reported here show that the event models are effective to filter out irrelevant photos and perform with a high accuracy on various social events categories.