Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Semantic Aspects for Cross-Media Image Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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Automatic image annotation has become an important and challenging problem due to the existence of semantic gap. In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of semantic image annotation and retrieval. In order to model training images precisely, we employ two PLSA models to capture semantic information from visual and textual modalities respectively. Then an adaptive asymmetric learning approach is proposed to fuse aspects which are learned from both modalities. For each image document, the weight of each modality is determined by its contribution to the content of the image. 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. Finally, we compare our approach with several previous approaches on a standard Corel dataset. The experiment results show that our approach performs more effective and accurate.