Discovering Temporal Patterns from Images using Extended PLSA

This paper considers the problem of modelling the topics in a sequence of images with known time stamp. Detecting and tracking of temporal data is an important task in multiple applications, such as finding hot research point from scientific literature, news article series analysis, email surveillance, search query log mining, etc. In contrast to existing works mainly focusing on text document collections, this paper considers mining temporal topic trends from image data set. An extension of the Probabilistic Latent Semantic Analysis(PLSA) model, which includes an additional variable associated with the time stamp to better model the temporal topics, is presented to track not only topics among images but also how topics change over time. Experiments show the effectiveness of this method.