Integrating image segmentation and annotation using supervised PLSA

In this work, we propose an integrated framework for image segmentation and annotation based on topic models. We first employ probabilistic latent semantic analysis (PLSA) to discover the topics of image regions and pixels, both utilized in our framework to segment images into different regions and to improve the annotation accuracy. Furthermore, we propose a supervised version of PLSA, SPLSA, as a new graphical model in order to accommodate the annotation results into the segmentation process to improve its performance. We compare the proposed SPLSA model with PLSA on their image segmentation performance, and also evaluate the image annotation performance of our methods on different datasets. The experimental results prove that the supervised information in SPLSA improves the segmentation results, and the image annotation accuracy is higher than the state-of-art methods including Conditional Random Fields.