Modeling and Analysis of Dynamic Behaviors of Web Image Collections
Can we model the temporal evolution of topics in Web image collections? 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 dynamics analysis. In this paper, we provide several experimental results to support the usefulness of image dynamics with the datasets of 47 topics gathered from Flickr. First, we produce some interesting observations such as tracking of subtopic evolution and outbreak detection, which cannot be achieved with conventional image sets. Second, we also present the complementary benefits that the images can introduce over the associated text analysis. Finally, we show that the training using the temporal association significantly improves the recognition performance.