Semi-supervised orthogonal discriminant projection for plant leaf classification

被引：19

作者：

Zhang, Shanwen ^{[1
]}

Lei, Yingke ^{[2
]}

Zhang, Chuanlei ^{[3
]}

Hu, Yihua ^{[2
]}

机构：

[1] Xijing Univ, Dept Elect & Informat Engn, Xian, Peoples R China

[2] Inst Elect Engn, Hefei, Peoples R China

[3] Tianjin Univ Sci & Technol, Sch Comp Sci & Informat Engn, Tianjin, Peoples R China

来源：

PATTERN ANALYSIS AND APPLICATIONS | 2016年 / 19卷 / 04期

基金：

美国国家科学基金会;

关键词：

Plant leaf classification; Dimensionality reduction; Orthogonal discriminant projection; Semi-supervised orthogonal discriminant projection; TEXTURE FEATURES; IMAGE RETRIEVAL;

D O I：

10.1007/s10044-015-0488-9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate.

引用

页码：953 / 961

页数：9

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