S. L. Feng, R. Manmatha and V. Lavrenko, “Multiple Bernoulli Relevance Models for Image and Video Annotation,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, 2004, pp. 1002-1009.

has been cited by the following article:

• TITLE: Combining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval

  AUTHORS: Zhixin Li, Zhenjun Tang, Weizhong Zhao, Zhiqing Li

  KEYWORDS: Automatic Image Annotation; Continuous PLSA; Semantic Gap; Hybrid Approach; Image Retrieval

  JOURNAL NAME: International Journal of Intelligence Science, Vol.2 No.3, July 27, 2012

  ABSTRACT: In order to bridge the semantic gap exists in image retrieval, this paper propose an approach combining generative and discriminative learning to accomplish the task of automatic image annotation and retrieval. We firstly present continuous probabilistic latent semantic analysis (PLSA) to model continuous quantity. Furthermore, we propose a hybrid framework which employs continuous PLSA to model visual features of images in generative learning stage and uses ensembles of classifier chains to classify the multi-label data in discriminative learning stage. Since the framework combines the advantages of generative and discriminative learning, it can predict semantic annotation precisely for unseen images. Finally, we conduct a series of experiments on a standard Corel dataset. The experiment results show that our approach outperforms many state-of-the-art approaches.