Leaf Image Recognition Based on Bag of Features

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one...

Full description

Bibliographic Details
Main Authors: Yaonan Zhang, Jing Cui, Zhaobin Wang, Jianfang Kang, Yufang Min
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/10/15/5177
_version_ 1797561179776745472
author Yaonan Zhang
Jing Cui
Zhaobin Wang
Jianfang Kang
Yufang Min
author_facet Yaonan Zhang
Jing Cui
Zhaobin Wang
Jianfang Kang
Yufang Min
author_sort Yaonan Zhang
collection DOAJ
description Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.
first_indexed 2024-03-10T18:10:25Z
format Article
id doaj.art-c427105d979844679f2003e687098362
institution Directory Open Access Journal
issn 2076-3417
language English
last_indexed 2024-03-10T18:10:25Z
publishDate 2020-07-01
publisher MDPI AG
record_format Article
series Applied Sciences
spelling doaj.art-c427105d979844679f2003e6870983622023-11-20T08:11:10ZengMDPI AGApplied Sciences2076-34172020-07-011015517710.3390/app10155177Leaf Image Recognition Based on Bag of FeaturesYaonan Zhang0Jing Cui1Zhaobin Wang2Jianfang Kang3Yufang Min4Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaNorthwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaPlants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.https://www.mdpi.com/2076-3417/10/15/5177feature extractionshape contextplant recognitionDPCNNBOF
spellingShingle Yaonan Zhang
Jing Cui
Zhaobin Wang
Jianfang Kang
Yufang Min
Leaf Image Recognition Based on Bag of Features
Applied Sciences
feature extraction
shape context
plant recognition
DPCNN
BOF
title Leaf Image Recognition Based on Bag of Features
title_full Leaf Image Recognition Based on Bag of Features
title_fullStr Leaf Image Recognition Based on Bag of Features
title_full_unstemmed Leaf Image Recognition Based on Bag of Features
title_short Leaf Image Recognition Based on Bag of Features
title_sort leaf image recognition based on bag of features
topic feature extraction
shape context
plant recognition
DPCNN
BOF
url https://www.mdpi.com/2076-3417/10/15/5177
work_keys_str_mv AT yaonanzhang leafimagerecognitionbasedonbagoffeatures
AT jingcui leafimagerecognitionbasedonbagoffeatures
AT zhaobinwang leafimagerecognitionbasedonbagoffeatures
AT jianfangkang leafimagerecognitionbasedonbagoffeatures
AT yufangmin leafimagerecognitionbasedonbagoffeatures