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

Hamrouni, Lamis; Kherfi, Mohammed Lamine; Aiadi, Oussama; Benbelghit, Abdellah

doi:10.3390/sym13091705

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…In this experiment, HC employed the aggregate clustering approach. Hamrouni et al (2021) proposed a plant leaf recognition method based on a hierarchical classification scheme, wherein the test image was first assigned to the nearest cluster and then matched to one class from the classes that fall under the determined cluster using a novel one-class learning classifier. The experimental results demonstrated the superiority of this method.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

The pistachio quality detection based on deep features plus unsupervised clustering

Liu,

Yang,

Zhu

et al. 2024

J Food Process Engineering

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The opening condition of the pistachio is a trait that has been sorted by machine vision. This study presented a qualitative and quantitative method for pistachio opening detection based on unsupervised learning. First, the convolution neural network ResNet18 was utilized to extract the pistachio features based on the different layers: bn4b_branch2b (bn4b), bn5b_branch2b (bn5b), and fc1000. The high‐dimensional feature was mapped to the low‐dimensional by t‐distributed stochastic neighbor embedding algorithm, and then, the clustering algorithms were utilized to cluster the image feature under different spatial dimensions. It was best that the clustering performance came from the bn5b feature layer. The accuracy of this method was 99.31% in three‐dimensional space. And completed the analysis of pistachio opening degree. This method exhibits significant potential for enhancing the quality detection and product classification of pistachios. The unsupervised clustering is utilized to sort not only pistachios but also general nuts.Practical ApplicationsIn this study, we proposed a quality detection method for pistachios based on unsupervised learning, which was capable of performing both qualitative and quantitative analyses of pistachio opening. This method provides an efficient quality detection and product classification strategy, with important practical value. The unsupervised clustering is utilized to sort not only pistachios but also general nuts. Furthermore, it significantly reduces the consumption of manpower and material resources, paving the way for the development of intelligent sorting equipment in the future, and has reference significance for the quality detection of other agricultural products.

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Section: Hierarchical Clusteringmentioning

confidence: 99%

The pistachio quality detection based on deep features plus unsupervised clustering

Liu,

Yang,

Zhu

et al. 2024

J Food Process Engineering

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“…Siamese CNN is a special type of convolutional neural network that is mainly used to process data that require pair comparison, such as determining whether two inputs are similar. Hamrouni et al [32] proposed to use Siamese training to distinguish the similarity and dissimilarity of the obtained samples. The study by Huang et al [33] suggested that the dual-path Siamese CNN could effectively leverage deep cellular neural networks even with limited training samples.…”

Section: Similarity Learningmentioning

confidence: 99%

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Liu,

Ling,

Liu

2024

Mathematics

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The development of medical image classification models necessitates a substantial number of labeled images for model training. In real-world scenarios, sample sizes are typically limited and labeled samples often constitute only a small portion of the dataset. This paper aims to investigate a collaborative similarity learning strategy that optimizes pseudo-labels to enhance model accuracy and expedite its convergence, known as the joint similarity learning framework. By integrating semantic similarity and instance similarity, the pseudo-labels are mutually refined to ensure their quality during initial training. Furthermore, the similarity score is utilized as a weight to guide samples away from misclassification predictions during the classification process. To enhance the model’s generalization ability, an adaptive consistency constraint is introduced into the loss function to improve performance on untrained datasets. The model achieved a satisfactory accuracy of 93.65% at 80% labeling ratio, comparable to supervised learning methods’ performance. Even with very low labeling ratio (e.g., 5%), the model still attained an accuracy of 74.28%. Comparison with other techniques such as Mean Teacher and FixMatch revealed that our approach significantly outperforms them in medical image classification tasks through improving accuracy by approximately 2%, demonstrating this framework’s leadership in medical image classification.

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“…Due to their excellent performance, pre-trained CNNs have been widely used in several computer vision tasks [31,32]. In the literature, there are various pre-trained architectures of CNN which have been trained on large-scale image databases.…”

Section: A Supervised Convolutional Neural Network Architecturementioning

confidence: 99%

Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis

et al. 2022

Self Cite

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Date fruit is among the major crops in the middle-east region, where millions of tons are harvested every year. Date is a healthy fruit, which involves sugars, minerals and vitamins. In addition, it helps preventing human body from several diseases such as cancer and heart diseases. Date sorting is a fundamental step in the date industry. However, manually conducting such an operation, by human labors, is expensive and time-consuming. In this paper, we propose a method for classifying the type of date fruit by incorporating supervised and unsupervised deep networks. Specifically, we use discriminant correlation analysis (DCA) algorithm to fuse features learned from convolution neural networks (VGG-F) and an unsupervised network called PCANet. DCA jointly performs feature fusion and dimensionality reduction with a low computational complexity. To carry out experiments, we introduce a new benchmark dataset of date fruit images from 20 date varieties. Our benchmark is, to the best of our knowledge, the largest one in terms of number of varieties. Note that the dataset is publicly available at https://unsat.000webhostapp.com/dataset . Experimental results demonstrate the utility of DCA as well as the complementarity of the fused features. It has also been shown the effectiveness of the proposed method compared to several relevant methods.

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

Cited by 8 publications

References 27 publications

The pistachio quality detection based on deep features plus unsupervised clustering

The pistachio quality detection based on deep features plus unsupervised clustering

Semi-Supervised Medical Image Classification with Pseudo Labels Using Coalition Similarity Training

Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis

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