  Photos are becoming spontaneous, objective, and universal sources of
information. This paper develops evolving situation recognition using photo
streams coming from disparate sources combined with the advances of deep
learning. Using visual concepts in photos together with space and time
information, we formulate the situation detection into a semi-supervised
learning framework and propose new graph-based models to solve the problem. To
extend the method for unknown situations, we introduce a soft label method
which enables the traditional semi-supervised learning framework to accurately
predict predefined labels as well as effectively form new clusters. To overcome
the noisy data which degrades graph quality, leading to poor recognition
results, we take advantage of two kinds of noise-robust norms which can
eliminate the adverse effects of outliers in visual concepts and improve the
accuracy of situation recognition. Finally, we demonstrate the idea and the
effectiveness of the proposed model on Yahoo Flickr Creative Commons 100
Million.
