Rotation-invariant feature extraction using a structural co-occurrence matrix

被引：39
作者：
Bezerra Ramalho, Geraldo L. [1 ]
Ferreira, Daniel S. [1 ,2 ]
Reboucas Filho, Pedro P. [1 ,2 ]
Sombra de Medeiros, Fatima N. [2 ]
机构：
[1] Inst Fed Educ Ciencia & Tecnol Ceara, Maracanau, CE, Brazil
[2] Univ Fed Ceara, Programa Posgrad Engn Teleinformat, Fortaleza, Ceara, Brazil
来源：
MEASUREMENT | 2016年 / 94卷
关键词：
Structural co-occurrence matrix; Feature extraction; Pattern recognition experiments; LOCAL BINARY PATTERN; TEXTURE; RECOGNITION; MOMENTS; SIMILARITY; TRANSFORM; PATH;
D O I：
10.1016/j.measurement.2016.08.012
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Feature extraction plays a key role in pattern recognition. Here we present a rotation-invariant feature extraction methodology based on a structural approach using co-occurrence statistics. We assessed the performance of our method comparing it to a gray-level co-occurrence matrix, local binary patterns and invariant moments in pattern recognition experiments. The results show that the proposed method provides an efficient and fast way to analyze digital images. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：406 / 415
页数：10
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