Supervised LDA for Image Annotation

Qiaojin Guo, Ning Li, Yu-Bin Yang, Gangshan Wu

Abstract: Region-based Image Annotation has received increasing attention in recent years. Topic models such as probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) have shown great success in object recognition and localization. In this paper, we introduce a supervised topic model for region-based image annotation. Images are segmented into superpixels, and visual features are extracted from each superpixel region. Boosted classifiers are then trained for each class, and the output of boosted classifiers are quantized as boosted visual words. The proposed model builds a generative model on both visual words and corresponding class labels. We tested the model on the 21-class MSRC dataset. Experimental results show that our model improves the annotation performance comparing with boosted classifiers.