CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Training products of experts by minimizing contrastive divergence
Neural Computation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Different Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Web image prediction using multivariate point processes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Dating historical color images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Time-sensitive web image ranking and retrieval via dynamic multi-task regression
Proceedings of the sixth ACM international conference on Web search and data mining
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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.