NDLI: Weed seeds recognition using Locally Linear Embedding

Content Provider	IEEE Xplore Digital Library
Author	FengFu Zhao Cheng Cai ShaoLi Huang DongJian He JunPing Zhu
Copyright Year	2009
Description	Author affiliation: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China (DongJian He; JunPing Zhu) \|\| College of Information Engineering, Northwest A&F University, Yangling, Shaanxi, China (FengFu Zhao; Cheng Cai; ShaoLi Huang)
Abstract	In this paper, a novel technique for the recognition of weed seeds, known as Locally Linear Embedding (LLE), is presented. This experiment contains two steps: dimensionality reduction with LLE and classification. In dimensionality reduction part, LLE, which is an unsupervised non-linear technique, is used to map high dimensional data into low dimensional, neighborhood-preserving embeddings and extract features. In classification part, minimum Euclidean distance classifier is used to recognize different weed seeds. This paper presents a comparison between LLE and PCA. Experimental results demonstrate that the representation of LLE is superior significantly as expected and gains much higher recognition rates, amounting to 96%, which is 8% higher than those of PCA.
Starting Page	59
Ending Page	62
File Size	744603
Page Count	4
File Format	PDF
ISBN	9781424446995
DOI	10.1109/ICTM.2009.5413004
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-05
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dimensionality reduction Digital images LLE Electronic equipment testing Euclidean classifier Weed seeds recognition Educational institutions Data mining Euclidean distance Production Feature extraction Mechanical variables measurement Agriculture Principal component analysis
Content Type	Text
Resource Type	Article

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