Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
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
The Journal of Machine Learning Research
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
PLSA-based image auto-annotation: constraining the latent space
Proceedings of the 12th annual ACM international conference on Multimedia
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold-ranking-based keyword propagation for image retrieval
EURASIP Journal on Applied Signal Processing
A Discriminative Kernel-Based Approach to Rank Images from Text Queries
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Annotating images and image objects using a hierarchical dirichlet process model
Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008
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
Ensemble approach based on conditional random field for multi-label image and video annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Automatic image annotation (AIA) is an effective technique to bridge the semantic gap between low level image features and high level semantics. However, most of the existing AIA approaches failed to consider the use of unlabeled data. In this paper, we present an interactive semi-supervised approach for AIA by integrating graph propagation model and kernel canonical correlation analysis (KCCA) together. We aim to jointly utilize the keywords associated with labeled and selected unlabeled images to annotate the residual unlabeled images. Toward this goal, we firstly estimate the annotations of unlabeled images by the consistency-driven graph propagation model. Then, the KCCA is applied to seek the semantic consistency between the two concurrent visual and textual features. In addition, the unlabeled image with highest semantic consistency is selected into the training set. Thus, with the enlarged training set, the potential of the semantic consistency between visual and textual representations could be boosted. Some experiments carried out on two standard databases validate the effectiveness of the proposed method.