Abstract

Can we model the temporal evolution of topics in Web im- age col lections ? If so, can we exploit the understanding of dynamics to solve novel visual problems or improve recognition performance ? These two challenging questions are the motivation for this work. We propose a nonparametric approach to modeling and analysis of topical evolution in image sets. A scalable and parallelizable sequential Monte Carlo based method is developed to construct the similarity network of a large-scale dataset that provides a base representation for wide ranges of dynam- ics analysis. In this paper, we provide several experimental results to support the usefulness of image dynamics with the datasets of 47 top- ics gathered from Flickr. First, we produce some interesting observations such as tracking of subtopic evolution and outbreak detection, which can- not be achieved with conventional image sets. Second, we also present the complementary benefits that the images can introduce over the asso- ciated text analysis. Finally, we show that the training using the temporal association significantly improves the recognition performance.