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Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network

Lamis Hamrouni, Mohammed Lamine Kherfi, Oussama Aiadi, Abdellah Benbelghit
2021 Symmetry  
In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering  ...  For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples.  ...  In [30] they performed a comparison between Siamese and CNN for plant species identification with small datasets.  ... 
doi:10.3390/sym13091705 fatcat:75cu5ykxnndbdpnlbjftvvgsda

Plant leaves classification: A few-shot learning method based on Siamese network

Bin Wang, Dian Wang
2019 IEEE Access  
In addition, a spatial structure optimizer (SSO) method for constructing the metric space is proposed, which will help to improve the accuracy of leaf classification.  ...  INDEX TERMS Leaf classification, few-shot learning, convolutional neural network, Siamese network. 151754 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Inspired by the Siamese network structure, this paper proposes a structure combining multilayer convolutional neural networks and few-shot learning methods to classify plant leaf VOLUME 7, 2019 species  ... 
doi:10.1109/access.2019.2947510 fatcat:22n7vzruhrf33cn75xlg2zqefi

Hidden Features: Experiments with Feature Transfer for Fine-Grained Multi-Class and One-Class Image Categorization

Varvara Vetrova, Sheldon Coup, Eibe Frank, Michael J. Cree
2018 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)  
a simple Siamese network trained only on data from the target domain.  ...  We investigate these questions by tackling two biological object recognition tasks: classification of "cryptic" plants of genus Coprosma and identification of New Zealand moth species.  ...  For each species, there are one to eight plants (mean 4.9) for a total of 83 plants, and there are typically ten images per plant (each of a different branch of the plant, mean 9.9), for a total of 819  ... 
doi:10.1109/ivcnz.2018.8634790 dblp:conf/ivcnz/VetrovaCFC18 fatcat:cf3ueagstvcvnihvcd37rbrmhu

GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end [article]

Ahmed Samy Nassar, Stefano D'Aronco, Sébastien Lefèvre, Jan D. Wegner
2020 arXiv   pre-print
Our method relies on a Graph Neural Network (GNN) to, detect all objects and output their geographic positions given images and approximate camera poses as input.  ...  Graph neural networks (GNN) naturally adapt to nongrid structured data like molecules, social networks, point clouds, or road networks.  ...  In [25] , for instance, authors propose to learn features using a siamese CNN for multi-modal inputs (images and optical flow maps).  ... 
arXiv:2003.10151v2 fatcat:neleyzbkpfhylbrusf76el5yry

FODA: Building Change Detection in High-Resolution Remote Sensing Images based on FeatureOutput Space Dual-Alignment

Yi Zhang, Min Deng, Fen He, Ya Guo, Geng Sun, Jie Chen
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
On the other hand, given the spatial context of image scene implicit in the output space, the ability to recognize pseudo-changes of the FODA is improved through an adversarial learning procedure.  ...  For example, BAM (Basic spatial temporal Attention Module), PAM (Pyramid spatial temporal Attention Module), and DASNet (Dual Attentive fully convolutional Siamese networks) enhance change detection performance  ...  Both methods use the structure of a deep Siamese network to extract features directly from the image pair. The difference lies in the different processing methods for feature maps [13] .  ... 
doi:10.1109/jstars.2021.3103429 fatcat:7camw6mpinbv5ei4wutbg4igni

Drought Stress Classification Using 3D Plant Models

Siddharth Srivastava, Swati Bhugra, Brejesh Lall, Santanu Chaudhury
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
using structure from motion on wheat plants.  ...  To overcome the high degree of self-similarities and selfocclusions in plant canopy, prior knowledge of leaf shape based on features from deep siamese network are used to construct an accurate 3D model  ...  These methods are used for creating synthetic plant structures but they do not capture the detailed structure of real plants and the parameters used for their synthesis are difficult to use for a non-expert  ... 
doi:10.1109/iccvw.2017.240 dblp:conf/iccvw/SrivastavaBLC17 fatcat:m4esh2je2ff2xj4why2nnmykbm

Drought Stress Classification using 3D Plant Models [article]

Siddharth Srivastava, Swati Bhugra, Brejesh Lall, Santanu Chaudhury
2017 arXiv   pre-print
using structure from motion on wheat plants.  ...  To overcome the high degree of self-similarities and self-occlusions in plant canopy, prior knowledge of leaf shape based on features from deep siamese network are used to construct an accurate 3D model  ...  These methods are used for creating synthetic plant structures but they do not capture the detailed structure of real plants and the parameters used for their synthesis are difficult to use for a non-expert  ... 
arXiv:1709.09496v2 fatcat:seaxwxo4wjd27onzsayd4myseq

A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests

Jianlei Kong, Chengcai Yang, Yang Xiao, Sen Lin, Kai Ma, Qingzhen Zhu, Xin Ning
2022 Computational Intelligence and Neuroscience  
With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification.  ...  Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases  ...  Machine Learning Technology for Plant Disease and Pest Identification.  ... 
doi:10.1155/2022/4391491 pmid:35665281 pmcid:PMC9162821 fatcat:2344c2ov3jb6pjf6jcobwk5fli

Dual Learning-Based Siamese Framework for Change Detection Using Bi-Temporal VHR Optical Remote Sensing Images

Bo Fang, Li Pan, Rong Kou
2019 Remote Sensing  
To the best of our knowledge, the idea of incorporating dual learning framework and Siamese network for change detection is novel.  ...  In this paper, motivated by this observation, we propose a novel hybrid end-to-end framework named dual learning-based Siamese framework (DLSF) for change detection.  ...  When the number of categories is large the number of samples for certain categories is small, the Siamese network is used to achieve identification and classification without predicting all the categories  ... 
doi:10.3390/rs11111292 fatcat:kkxz6yz5tjc3hcuhgakjrsxpgu

A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features

Lijun Huang, Ru An, Shengyin Zhao, Tong Jiang, Hao Hu
2020 Remote Sensing  
The results revealed that our method outperforms SVM and Siamese Network.  ...  The proposed method is compared with Support Vector Machine (SVM) and Siamese Network, and the check error rate dropped to 7.86%, while the Kappa increased to 0.8254.  ...  Method Testing on Public Dataset Comparing with SVM and Siamese Network For the comparison of the proposed method with SVM and Siamese Network [43–45], the public dataset was downloaded from a website  ... 
doi:10.3390/rs12091441 fatcat:qy67zdkkjnd63k4kzogudmal5m

A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

Hao Chen, Zhenwei Shi
2020 Remote Sensing  
In our work, we propose a novel Siamese-based spatial–temporal attention neural network.  ...  In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial–temporal dependency, we design a CD self-attention mechanism to model the spatial–temporal  ...  We propose a spatial-temporal attention neural network (STANet) for CD, which belongs to the metric-based method. The Siamese FCN is employed to extract the bitemporal image feature maps.  ... 
doi:10.3390/rs12101662 fatcat:p3uq7rpqdfdgdp3i6pbd5pcelm

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery [article]

Xin Zhang, Liangxiu Han, Tam Sobeih, Lewis Lappin, Mark Lee, Andew Howard, Aron Kisdi
2022 arXiv   pre-print
The proposed SSVT introduces a Spectral Attention Block (SAB) and a Spatial Interaction Block (SIB), which allows for simultaneous learning of both spatial and spectral features from UAV digital aerial  ...  Destructive approaches based on plant tissue analysis are time consuming and impractical over large fields.  ...  General network structure and detailed block structure for CNN models. evaluations and comparisons, we select 224 × 224 as the input size.  ... 
arXiv:2111.06839v2 fatcat:mnoeuqfkzvbmpouusbkrfzn3qy

History and geography of identifications related to resource conflicts and ethnic violence in Northern Thailand

Chusak Wittayapak
2008 Asia Pacific Viewpoint  
To understand this phenomenon, this chapter takes incidents of violence in Northern Thailand as a point of departure to explain how the historical construction of ethnic identification is tied to the spatial  ...  aggravated to the point that violence has been perpetuated against ethnic highlanders by lowlanders who have adopted orthodox science and nationalist sentiments drawn from a history and geography of ethnic identification  ...  For instance, the community forest networks in Northern Thailand has aimed to ordain 50 million trees in contest with the government plan to plant 50 million rai of forest (8 million hectares).  ... 
doi:10.1111/j.1467-8373.2008.00364.x fatcat:bdkkwhiybnbjtm6kzsoa4rtlge

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Morena M. Tinte, Kekeletso H. Chele, Justin J. J. van der Hooft, Fidele Tugizimana
2021 Metabolites  
Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration.  ...  plant responses to abiotic stress conditions.  ...  , MS2DeepScore was introduced that uses a Siamese neural network to predict the structural similarity between two chemical structures solely based on their MS/MS fragmentation spectra [238] .  ... 
doi:10.3390/metabo11070445 fatcat:grizhdxgnzcaxlcwr2octo2wdy

Special issue on extreme learning machine and deep learning networks

Zhihong Man, Guang-Bin Huang
2020 Neural computing & applications (Print)  
In ''Hierarchical attentive Siamese network for real-time visual tracking'', the authors present a novel hierarchical attentive Siamese (HASiam) network for high-performance visual tracking.  ...  The experimental results show that the proposed algorithm can perform well for material identification.  ... 
doi:10.1007/s00521-020-05175-0 fatcat:4a6v6gptyzhy5ncwnmhuvhgwqq
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