Learning Hybrid Models for Image Annotation with Partially Labeled Data
Xuming He, Richard S. Zemel · 2008
Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model frame-work for utilizing partially labeled data that integrates a generative topic model for image appearance with discriminative label prediction. We propose three al-ternative formulations for imposing a spatial smoothness prior on the image la-bels. Tests of the new models and some baseline approaches on three real image datasets demonstrate the effectiveness of incorporating the latent structure. 1