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
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
A graph-based image annotation framework
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Computers in Biology and Medicine
Image annotation via graph learning
Pattern Recognition
A New Baseline for Image Annotation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
International Journal of Computer Vision
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Pattern Recognition Letters
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Image classification by a two-dimensional hidden Markov model
IEEE Transactions on Signal Processing
Combining Context, Consistency, and Diversity Cues for Interactive Image Categorization
IEEE Transactions on Multimedia
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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This paper presents a generalized relevance model for automatic image annotation through learning the correlations between images and annotation keywords. Different from previous relevance models that can only propagate keywords from the training images to the test ones, the proposed model can perform extra keyword propagation among the test images. We also give a convergence analysis of the iterative algorithm inspired by the proposed model. Moreover, to estimate the joint probability of observing an image with possible annotation keywords, we define the inter-image relations through proposing a new spatial Markov kernel based on 2D Markov models. The main advantage of our spatial Markov kernel is that the intra-image context can be exploited for automatic image annotation, which is different from the traditional bag-of-words methods. Experiments on two standard image databases demonstrate that the proposed model outperforms the state-of-the-art annotation models.