Fuzzy Extended Feature Line Classifier for Image Classification

被引：1

作者：

Pan, Jeng-Shyang ^{[1
]}

Chu, Shu-Chuan ^{[2
]}

Yan, Lijun ^{[3
]}

Tang, Linlin ^{[4
]}

机构：

[1] Fujian Univ Technol, Coll Informat Sci & Engn, Fuzhou, Peoples R China

[2] Flinders Univ S Australia, Sch Comp Sci Engn & Math, Adelaide, SA, Australia

[3] Shenzhen Inst Informat Technol, Sch Comp Sci, Shenzhen, Peoples R China

[4] Harbin Inst Technol, Shenzhen Grad Sch, Sch Comp Sci & Technol, Shenzhen, Peoples R China

来源：

2015 INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP) | 2015年

关键词：

Nearest feature line; Fuzzy KNN; Image classification; NEAREST FEATURE LINE; PATTERN-CLASSIFICATION; RECOGNITION; RETRIEVAL; SEGMENT;

D O I：

10.1109/IIH-MSP.2015.95

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new approach called Fuzzy Extended Feature Line (FEFL) is proposed for image classification in this paper, which retain the advantages and ideas of Nearest Feature Line (NFL). The proposed FEFL use NFL to extend the prototype image sample set. Fuzzy K-Nearest Neighbor is applied for adding the new suitable samples to the prototype sample set. Experimental results on ORL face database and finger-knuckle-print database demonstrate the effectiveness of the proposed algorithm.

引用

下载

页码：290 / 293

页数：4

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