Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Proceedings of the international workshop on Workshop on multimedia information retrieval
Bipartite graph reinforcement model for web image annotation
Proceedings of the 15th international conference on Multimedia
Cross-media manifold learning for image retrieval & annotation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Visual topic model for web image annotation
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Bidirectional-isomorphic manifold learning at image semantic understanding & representation
Multimedia Tools and Applications
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Does there exist a compact set of keywords that can completely and effectively cover the image annotation problem by expanding from it? In this paper, we answer this question by presenting a complete set framework for image annotation, which is motivated by the existence of semantic ontology. To generate this set, we propose a cross model optimization strategy from both textual and visual information for topic decomposition, based on a so-called Bipartite LSA model, which minimize multimodal error energy functions in a probabilistic Latent Semantic Analysis model. To achieve complete set based annotation, we present a Gaussian-Kernel-Generative process based keyword generation procedure, which analogizes keyword annotation in a probabilistic generative manner. A group of experiments is performed on Washington University image database and 80,000 Flickr images with comparisons to the state-of-the-arts. Finally, potential advantages and future improvements of our framework are discussed outside the scope of topic modeling.