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...
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MDPI AG
2020-07-01
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Online Access: | https://www.mdpi.com/2076-3417/10/15/5177 |
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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 |
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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 |