We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
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...
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Abstract -We study the problem of communication reliability and diversity in multi-hop wireless networks. Our aim is to develop a new network model that better takes into account t...
Amir Ehsan Khandani, Jinane Abounadi, Eytan Modian...
In this paper, we address invariant keypoint-based texture characterization and recognition. Viewing keypoint sets associated with visual textures as realizations of point process...