MULTIVARIATE LOG-GAUSSIAN COX MODELS OF ELEMENTARY SHAPES FOR RECOGNIZING NATURAL SCENE CATEGORIES

被引：0
|
作者：
Huu-Giao Nguyen [1 ]
Fablet, Ronan
Boucher, Jean-Marc [2 ]
机构：
[1] Telecom Bretagne, Inst Telecom, LabSTICC, CS 83818, F-29238 Brest 3, France
[2] Univ Europe Bretagne, Bretagne, France
来源：
2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2011年
关键词：
log-Gaussian Cox process; topographic map; inner-distance shape context; scene recognition;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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. Reported results validate the proposed descriptor with respect to previous work.
引用
收藏
页码：665 / 668
页数：4
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