Multi-Modal Hierarchical Dirichlet Process Model for Predicting Image Annotation and Image-Object Label Correspondence.

Many real-world applications call for learning predictive relationships from multi-modal data. In particular, in multi-media and web applications, given a dataset of images and their associated captions, one might want to construct a predictive model that not only predicts a caption for the image but also labels the individual objects in the image. We address this problem using a multi-modal hierarchical Dirichlet Process model (MoM-HDP) - a stochastic process for modeling multimodal data. MoM-HDP is an analog of a multi-modal Latent Dirichlet Allocation (MoM-LDA) with an infinite number of mixture components. Thus MoM-HDP allows circumventing the need for a priori choice of the number of mixture components or the computational expense of model selection. 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. The mo...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Oksana Yakhnenko, Vasant Honavar
• Keywords: Computer Science, Dirichlet Process Model, Multi-modal Latent Dirichlet, Predictive Model, SDM 2009