We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them. Our approach couples topic models originally developed...
Erik B. Sudderth, Antonio Torralba, William T. Fre...
In this paper, we contribute a new paradigm of combining string recognizers and propose generic frameworks for hierarchical and parallel combination of multiple string recognizers...
— Most application data units are too large to be carried in a single packet (or cell) and must be segmented for network delivery. To an application, the end-to-end delays and lo...
—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as...
Gustavo Carneiro, Antoni B. Chan, Pedro J. Moreno,...
We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects....