Hierarchical density estimation for image classification
Li, Zhen
Loading…
Permalink
https://hdl.handle.net/2142/18612
Description
Title
Hierarchical density estimation for image classification
Author(s)
Li, Zhen
Issue Date
2011-01-21T22:51:40Z
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Hierarchical maximum a posteriori (MAP) estimation
random forest
localized Gaussian
Abstract
"Histogram (bag-of-words) and Gaussian mixture models (GMMs) have been widely used in patch-based image classification problems. Despite the satisfactory results reported, both methods suffer from a number of disadvantages. For instance, a histogram may be easy to learn but has a large quantization error; on the contrary, Gaussian mixture model based methods have better modeling capabilities but are inefficient in both learning and testing. In this thesis, we present a novel hierarchical density estimation approach for image classification. This new approach partitions the feature space into small regions using a tree structure. For each region, ""local"" distribution is characterized by class-conditional Gaussians via hierarchical maximum a posteriori (MAP) estimation. We further enhance the parameter estimation by smoothing over a collection of randomized trees. This new approach enjoys the merits of superior modeling capability, robust parameter estimation, and efficient testing. Experiments on scene classification demonstrate both the effectiveness and efficiency of this new approach."
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
