By Topic

Multivariate log-Gaussian Cox models of elementary shapes for recognizing natural scene categories

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Huu-Giao Nguyen ; LabSTICC, Inst. Telecom, Brest, France ; Fablet, R. ; Boucher, J.-M.

In this paper, we address invariant scene classification from images. We propose a novel descriptor based on the statistical characterization of the spatial patterns formed by elementary objects in images. Elementary objects are defined from a tree of shapes of the topology map of the image and each object is characterized by shape context feature vector. Viewing the set of elementary objects as a realization of a random spatial process, we investigate a statistical analysis using log- Gaussian Cox model to define an invariant image descriptor. An application to natural scene recognition is described. Re- ported results validate the proposed descriptor with respect to previous work.

Published in:

Image Processing (ICIP), 2011 18th IEEE International Conference on

Date of Conference:

11-14 Sept. 2011