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
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Effective automatic image annotation via a coherent language model and active learning
Proceedings of the 12th annual ACM international conference on Multimedia
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Universal and Adapted Vocabularies for Generic Visual Categorization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotating Images by Mining Image Search Results
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
Effective Image Retrieval Based on Hidden Concept Discovery in Image Database
IEEE Transactions on Image Processing
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 proposes an automatic image annotation method based on concept-specific image representation and discriminative learning. Firstly, the concept-specific visual vocabularies are generated by assuming that localized features from the images with a specific concept are of the distribution of Gaussian Mixture Model (GMM). Each component in the GMM is taken as a visual token of the concept. The visual tokens of all the concepts are clustered to obtain a universal token set. Secondly, the image is represented as a concept-specific feature vector by computing the average posterior probabilities of being each universal visual token for all the localized features and assigning it to corresponding concept-specific visual tokens. Thus the feature vector for an image varies with different concepts. Finally, we implement image annotation and retrieval under a discriminative learning framework of Bayesian classifiers, Max-Min posterior Pseudo-probabilities (MMP). The proposed method were evaluated on the popular Corel-5K database. The experimental results with comparisons to state-of-the-art show that our method is promising.