A complex network-based approach for boundary shape analysis

被引：88
|
作者：
Backes, Andre Ricardo [1 ]
Casanova, Dalcimar [1 ]
Bruno, Odemir Martinez [1 ]
机构：
[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, Sao Carlos, SP, Brazil
来源：
PATTERN RECOGNITION | 2009年 / 42卷 / 01期
基金：
巴西圣保罗研究基金会;
关键词：
Shape analysis; Shape recognition; Complex network; Small-world model; RECOGNITION; FOURIER;
D O I：
10.1016/j.patcog.2008.07.006
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：54 / 67
页数：14
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