Name
Abstract | ||
|---|---|---|
We develop hierarchical, probabilistic models for objects, the parts composing them, and the visual scenes surrounding them. Our approach couples topic models originally developed for text analysis with spatial transformations, and thus consistently accounts for geometric constraints. By building integrated scene models, we may discover contextual relationships, and better exploit partially labeled training images. We first consider images of isolated objects, and show that sharing parts among object categories improves detection accuracy when learning from few examples. Turning to multiple object scenes, we propose nonparametric models which use Dirichlet processes to automatically learn the number of parts underlying each object category, and objects composing each scene. The resulting transformed Dirichlet process (TDP) leads to Monte Carlo algorithms which simultaneously segment and recognize objects in street and office scenes. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11263-007-0069-5 | International Journal of Computer Vision |
Keywords | Field | DocType |
Object recognition,Dirichlet process,Hierarchical Dirichlet process,Transformation,Context,Graphical models,Scene analysis | Object detection,Hierarchical Dirichlet process,Computer vision,Dirichlet process,Computer science,Scene statistics,Artificial intelligence,Topic model,Graphical model,Probabilistic logic,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
77 | 1-3 | 0920-5691 |
Citations | PageRank | References |
98 | 7.70 | 39 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erik B. Sudderth | 1 | 1420 | 119.04 |
| Antonio Torralba | 2 | 14607 | 956.27 |
| William T. Freeman | 3 | 17382 | 1968.76 |
| Alan S. Willsky | 4 | 7466 | 847.01 |