Image sequence analysis using dynamic topic model

A topic model is a probabilistic method for data analysis and characterization that provides insight into the topics that comprise each document in a corpus, where each topic is described by an associated word distribution. A dynamic topic model is an extension of this model that can be applied to time series data. These models have typically been applied to the text domain where the concepts of tokens and words are well defined. Applying these models to the image domain is non-obvious because the concepts of tokens and words need to hand-crafted. In this work, we apply the dynamic topic model to a sequence of images to provide insight into their dynamic nature, e.g., by helping to identify interesting locations in time that correspond to change in operating conditions We apply this model to images from the KITTI dataset and show that the model captures the evolving nature of these topics over time.