In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of automatic image annotation. In order to model training data precisely, we represent an image as a bag of visual words and employ two PLSA models to capture semantic information from visual and textual modalities respectively. Furthermore, an adaptive learning approach is proposed to combine the aspects learned from both modalities. For each image document, distribution over aspects is fused by different weight in terms of the entropy of its feature distribution. Consequently, the two models are linked with the same distribution over aspects. This structure can predict semantic annotation for an unseen image because it associates visual and textual modalities properly. We compare our approach with several previous approaches on a standard Corel dataset. The experiment results show that our approach performs more effectively and accurately.
['Zhixin Li', 'Zhiping Shi', 'Zhiqing Li', 'Zhongzhi Shi']
Modeling latent aspects for automatic image annotation