Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Probabilistic models for topic learning from images and captions in online biomedical literatures
Proceedings of the 18th ACM conference on Information and knowledge management
Image annotation with tagprop on the MIRFLICKR set
Proceedings of the international conference on Multimedia information retrieval
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Mining partially annotated images
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An interactive semi-supervised approach for automatic image annotation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
A picture is worth a thousand tags: automatic web based image tag expansion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Many applications call for learning to label individual objects in an image where the only information available to the learner is a dataset of images with their associated captions, i.e., words that describe the image content without specifically labeling the individual objects. We address this problem using a multi-modal hierarchical Dirichlet process model (MoM-HDP) - a nonparametric Bayesian model which provides a generalization for multi-model latent Dirichlet allocation model (MoM-LDA) used for similar problems in the past. We apply this model for predicting labels of objects in images containing multiple objects. During training, the model has access to an un-segmented image and its caption, but not the labels for each object in the image. The trained model is used to predict the label for each region of interest in a segmented image. MoM-HDP generalizes a multi-modal latent Dirichlet allocation model in that it allows the number of components of the mixture model to adapt to the data. The model parameters are efficiently estimated using variational inference. Our experiments show that MoM-HDP performs just as well as or better than the MoM-LDA model (regardless the choice of the number of clusters in the MoM-LDA model).