28 citations · 68 across the 10 of their papers we have counts for
7 papers · 1 filter
Direct Estimation of Appearance Models for Segmentation
Jeova F. S. Rocha Neto, Pedro Felzenszwalb, Marilyn Vazquez
Image segmentation algorithms often depend on appearance models that characterize the distribution of pixel values in different image regions. We describe a new approach for estima…
Spectral Image Segmentation with Global Appearance Modeling
Jeova F. S. Rocha Neto, Pedro F. Felzenszwalb
We introduce a new spectral method for image segmentation that incorporates long range relationships for global appearance modeling. The approach combines two different graphs, one…
Scene Grammars, Factor Graphs, and Belief Propagation
Jeroen Chua, Pedro F. Felzenszwalb
We describe a general framework for probabilistic modeling of complex scenes and inference from ambiguous observations. The approach is motivated by applications in image analysis…
Generalized Majorization-Minimization
Sobhan Naderi Parizi, Kun He, Reza Aghajani +2
Non-convex optimization is ubiquitous in machine learning. Majorization-Minimization (MM) is a powerful iterative procedure for optimizing non-convex functions that works by optimi…
Automatic Discovery and Optimization of Parts for Image Classification
Sobhan Naderi Parizi, Andrea Vedaldi, Andrew Zisserman +1
Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of inf…
Multiscale Fields of Patterns
Pedro F. Felzenszwalb, John G. Oberlin
We describe a framework for defining high-order image models that can be used in a variety of applications. The approach involves modeling local patterns in a multiscale representa…
A Stochastic Grammar for Natural Shapes
Pedro F. Felzenszwalb
We consider object detection using a generic model for natural shapes. A common approach for object recognition involves matching object models directly to images. Another approach…