Latent topic random fields: Learning using a taxonomy of labels

An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn from a semantic hierarchy, and can also readily cope with missing labels, and roughly-specified object boundaries. We introduce a new form of latent topic model, learning a novel context representation in the joint labeland-image space by capturing co-occurring patterns within and between image features and object labels. Given a topic, the model generates the input data, as well as a topicdependent probabilistic classifier to predict labels for image regions. We present results on two real-world datasets, demonstrating significant improvements gained by including the coarsely labeled images.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Xuming He, Richard S. Zemel
• Keywords: Computer Vision, CVPR 2008, Image Labeling Concerns, Image Regions, Latent Topic Model, Roughly-specified Object Boundaries, Topicdependent Probabilistic Classifier