Title :
Multivariate log-Gaussian Cox models of elementary shapes for recognizing natural scene categories
Author :
Nguyen, Huu-Giao ; Fablet, Ronan ; Boucher, Jean-Marc
Author_Institution :
LabSTICC, Inst. Telecom, Brest, France
Abstract :
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.
Keywords :
Gaussian processes; image classification; image recognition; natural scenes; random processes; solid modelling; statistical analysis; trees (mathematics); elementary objects; elementary shapes; invariant image descriptor; invariant scene classification; multivariate log-Gaussian Cox model; natural scene category recognition; random spatial process realization; shape context feature vector; spatial pattern characterization; statistical analysis; statistical characterization; topology map; Conferences; Context; Correlation; Probabilistic logic; Shape; Training; Visualization; inner-distance shape context; log-Gaussian Cox process; scene recognition; topographic map;
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2011.6116640
