Plant Leaf Recognition through Local Discriminative Tangent Space Alignment

Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discrimina...

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Main Authors: Chuanlei Zhang, Shanwen Zhang, Weidong Fang
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2016/1989485
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author Chuanlei Zhang
Shanwen Zhang
Weidong Fang
author_facet Chuanlei Zhang
Shanwen Zhang
Weidong Fang
author_sort Chuanlei Zhang
collection DOAJ
description Manifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.
format Article
id doaj-art-40ec3bd45bc14b4b9bf6f95183adc2b6
institution Kabale University
issn 2090-0147
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language English
publishDate 2016-01-01
publisher Wiley
record_format Article
series Journal of Electrical and Computer Engineering
spelling doaj-art-40ec3bd45bc14b4b9bf6f95183adc2b62025-02-03T01:28:09ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552016-01-01201610.1155/2016/19894851989485Plant Leaf Recognition through Local Discriminative Tangent Space AlignmentChuanlei Zhang0Shanwen Zhang1Weidong Fang2School of Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, ChinaDepartment of Electronics and Information Engineering, Xijing University, Xi’an 710123, ChinaKey Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, ChinaManifold learning based dimensionality reduction algorithms have been payed much attention in plant leaf recognition as the algorithms can select a subset of effective and efficient discriminative features in the leaf images. In this paper, a dimensionality reduction method based on local discriminative tangent space alignment (LDTSA) is introduced for plant leaf recognition based on leaf images. The proposed method can embrace part optimization and whole alignment and encapsulate the geometric and discriminative information into a local patch. The experiments on two plant leaf databases, ICL and Swedish plant leaf datasets, demonstrate the effectiveness and feasibility of the proposed method.http://dx.doi.org/10.1155/2016/1989485
spellingShingle Chuanlei Zhang
Shanwen Zhang
Weidong Fang
Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
Journal of Electrical and Computer Engineering
title Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
title_full Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
title_fullStr Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
title_full_unstemmed Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
title_short Plant Leaf Recognition through Local Discriminative Tangent Space Alignment
title_sort plant leaf recognition through local discriminative tangent space alignment
url http://dx.doi.org/10.1155/2016/1989485
work_keys_str_mv AT chuanleizhang plantleafrecognitionthroughlocaldiscriminativetangentspacealignment
AT shanwenzhang plantleafrecognitionthroughlocaldiscriminativetangentspacealignment
AT weidongfang plantleafrecognitionthroughlocaldiscriminativetangentspacealignment