Segmentation of objects in a detection window by Nonparametric Inhomogeneous CRFs

  • Authors:

  • Bo Yang;Chang Huang;Ram Nevatia

  • Affiliations:

  • University of Southern California, Institute for Robotics and Intelligent Systems Los Angeles, CA 90089, USA;University of Southern California, Institute for Robotics and Intelligent Systems Los Angeles, CA 90089, USA;University of Southern California, Institute for Robotics and Intelligent Systems Los Angeles, CA 90089, USA

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a method for segmenting objects of a specific class in a given detection window. The task is to label each pixel as belonging to the foreground or the background. We pose the problem as that of finding the maximum a posterior (MAP) estimation in a modified form of Conditional Random Field model that we call a Nonparametric Inhomogeneous CRF (NICRFs). An NICRF, like a conventional CRF, has nodes representing pixels and pairwise links connecting neighboring pixels; however, both the unary and pairwise energy terms are inhomogeneous in the sense of being dependent on pixel positions to account for prior information of the known object class. It differs from earlier methods in that position information is in form of unique term functions for each individual pixel, rather than the same parametric function but with varying parameters. Unary terms are given by a learned boosted classifier based on novel Adaptive Edgelet Features (AEFs) for inferring probability of a pixel being foreground; pairwise terms are learned by joint probabilities for neighboring pixels as a function of contrast; a monotonicity constraint is used to reduce possible over-fit effects. We expand the neighborhood used for pairwise terms, and add inhomogeneous weighting factors for different pairwise terms. We use the Loopy Belief Propagation (LBP) algorithm for MAP estimation. A local search process is proposed to deal with inaccurate detection windows. We evaluate our approach on examples of pedestrians and cars and demonstrate significant improvements compared to earlier methods.