28 citations · 68 across the 10 of their papers we have counts for
14 papers
Convex Combination Belief Propagation Algorithms
Anna Grim, Pedro Felzenszwalb
We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief prop…
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…
Clustering with Semidefinite Programming and Fixed Point Iteration
Pedro Felzenszwalb, Caroline Klivans, Alice Paul
We introduce a novel method for clustering using a semidefinite programming (SDP) relaxation of the Max k-Cut problem. The approach is based on a new methodology for rounding the s…
Iterated Linear Optimization
Pedro Felzenszwalb, Caroline Klivans, Alice Paul
We introduce a fixed point iteration process built on optimization of a linear function over a compact domain. We prove the process always converges to a fixed point and explore th…
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…
Flow-firing processes
Pedro Felzenszwalb, Caroline Klivans
We consider a discrete non-deterministic flow-firing process for rerouting flow on the edges of a planar complex. The process is an instance of higher-dimensional chip-firing. In t…
Similar Elements and Metric Labeling on Complete Graphs
Pedro F. Felzenszwalb
We consider a problem that involves finding similar elements in a collection of sets. The problem is motivated by applications in machine learning and pattern recognition. We formu…
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…
Diffusion Methods for Classification with Pairwise Relationships
Pedro F. Felzenszwalb, Benar F. Svaiter
We define two algorithms for propagating information in classification problems with pairwise relationships. The algorithms are based on contraction maps and are related to non-lin…
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…
The Generalized A* Architecture
P. F. Felzenszwalb, D. McAllester
We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in thi…