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Multidimensional binary search trees used for associative searching
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An efficient boosting algorithm for combining preferences
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Distinctive Image Features from Scale-Invariant Keypoints
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Optimal multimodal fusion for multimedia data analysis
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Face Recognition Using Laplacianfaces
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Dual cross-media relevance model for image annotation
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Tag recommendations based on tensor dimensionality reduction
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MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning tag relevance by neighbor voting for social image retrieval
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Information theoretic measures for clusterings comparison: is a correction for chance necessary?
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Tag refinement by regularized LDA
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SIAM Review
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
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Multimodal pLSA on visual features and tags
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GeoFolk: latent spatial semantics in web 2.0 social media
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Multi-modal multi-label semantic indexing of images based on hybrid ensemble learning
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Multi modal semantic indexing for image retrieval
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Unified tag analysis with multi-edge graph
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Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Information Sciences: an International Journal
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Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
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This work presents a method for the efficient indexing of tagged images. Tagged images are a common resource of social networks and occupy a large portion of the social media stream. Their basic characteristic is the co-existence of two heterogeneous information modalities, i.e. visual and tag, which refer to the same abstract meaning. This multi-modal nature of tagged images makes their efficient indexing a challenging task that apart from dealing with the heterogeneity of modalities, it needs to also exploit their complementary information capacity. Towards this objective, we propose the extension of probabilistic latent semantic analysis to higher order, so as to become applicable for more than two observable variables. Then, by treating images, visual features and tags as the three observable variables of an aspect model, we learn a space of latent topics that incorporates the semantics of both visual and tag information. Our novelty is on using the cross-modal dependencies learned from a corpus of images to approximate the joint distribution of the observable variables. By penalizing the co-existence of visual content and tags that are known from experience to exhibit low dependency, we manage to filter out the effect of noisy content in the resulting latent space.