Modified locally linear discriminant embedding for plant leaf recognition

被引：43

作者：

Zhang, Shanwen ^{[1
]}

Lei, Ying-Ke ^{[1
,2
,3
]}

机构：

[1] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China

[2] Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China

[3] Inst Elect Engn, Hefei 230027, Anhui, Peoples R China

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 14-15期

基金：

美国国家科学基金会;

关键词：

Plant leaf recognition; Locally linear embedding (LLE); Modified maximizing margin criterion (MM MC); Modified locally linear discriminant embedding (MLLDE); Manifold learning; PROBABILISTIC NEURAL-NETWORKS; DIMENSIONALITY REDUCTION; FEATURE-EXTRACTION; TEXTURE FEATURES; FACE RECOGNITION; IMAGE RETRIEVAL; EIGENFACES; EIGENMAPS; EFFICIENT; MODEL;

D O I：

10.1016/j.neucom.2011.03.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Based on locally linear embedding (LLE) and modified maximizing margin criterion (MMMC), a modified locally linear discriminant embedding (MLLDE) algorithm is proposed for plant leaf recognition in this paper. By MLLDE, the plant leaf images are mapped into a leaf subspace for analysis, which can detect the essential leaf manifold structure. Furthermore, the unwanted variations resulting from changes in period, location, and illumination can be eliminated or reduced. Different from principal component analysis (PCA) and linear discriminant analysis (LDA), which can only deal with flat Euclidean structures of plant leaf space, MLLDE not only inherits the advantages of locally linear embedding (LLE), but makes full use of class information to improve discriminant power by introducing translation and rescaling models. The experimental results on real plant leaf database show that the MLLDE is effective for plant leaf recognition. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：2284 / 2290

页数：7

共 50 条

[21] Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
Zhang, Shanwen
Chau, Kwok-Wing
EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, PROCEEDINGS, 2009, 5754 : 948 - 955
[22] Orthogonal locally discriminant projection for classification of plant leaf diseases
Zhang, Shanwen
Zhang, Chuanlei
2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), 2013, : 241 - 245
[23] Modified Linear Discriminant Analysis for speech recognition
Li, Xiao-Bing
O'Shaughnessy, Douglas
2007 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, VOLS 1-3, 2007, : 1598 - 1601
[24] Gene expression data classification using locally linear discriminant embedding
Li, Bo
Zheng, Chun-Hou
Huang, De-Shuang
Zhang, Lei
Han, Kyungsook
COMPUTERS IN BIOLOGY AND MEDICINE, 2010, 40 (10) : 802 - 810
[25] Discriminant locally linear embedding with high-order tensor data
Li, Xuelong
Lin, Stephen
Yan, Shuicheng
Xu, Dong
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2008, 38 (02): : 342 - 352
[26] Orthogonal Linear Local Spline Discriminant Embedding for Face Recognition
Lei, Ying-Ke
Hu, Rong-Xiang
Tang, Lei
Zhang, Shan-Wen
Huang, De-Shuang
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, 2010,
[27] Face recognition based on extended locally linear embedding
Zhu, L.
Zhu, S. A.
2006 1ST IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-3, 2006, : 871 - +
[28] Supervised locally linear embedding algorithm for pattern recognition
Kouropteva, O
Okun, O
Pietikäinen, M
PATTERN RECOGNITION AND IMAGE ANALYSIS, PROCEEDINGS, 2003, 2652 : 386 - 394
[29] Research of Face Recognition Based on Locally Linear Embedding
Zhou Cuihong
Yang Gelan
SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING, VOL 2, PROCEEDINGS, 2009, : 109 - 111
[30] Face recognition based on extended locally linear embedding
Zhu, L.
Zhu, S. A.
ICIEA 2006: 1ST IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-3, PROCEEDINGS, 2006, : 382 - 385

← 1 2 3 4 5 →