Depth Learning with Convolutional Neural Network for Leaves Classifier Based on Shape of Leaf Vein

Thanh, Tan Kiet Nguyen; Truong, Quoc Bao; Truong, Quoc Dinh; Xuan, Hiep Huynh

doi:10.1007/978-3-319-75417-8_53

Cited by 6 publications

(2 citation statements)

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“…Chaki et al [20] proposed a novel approach by using the combination of fuzzy-color and edge-texture histogram to recognize fragmented leaf images. Some features based on Gabor filters [21,22], fractal dimension [23], locality projection analysis (SLPA) [24], kernel based principal component analysis (KPCA) [25], bag of word (BOW) [22,26] and convolutional neural networks (CNN) [27] are also used for leaf recognition.…”

Section: Introductionmentioning

confidence: 99%

Leaf Image Recognition Based on Bag of Features

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Leaf Image Recognition Based on Bag of Features

et al. 2020

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“…Recently, due to the excellent performance of deep learning convolutional neural networks in the field of computer vision, they has become the main means to solve the problems of image classification, image recognition and semantic segmentation [14]- [16]. The application of deep learning methods in plant classification has achieved a good performance, and their comprehensive performance is better than that of most manual feature extraction classification methods, especially their excellent generalization performance [17]- [19].…”

Section: Introductionmentioning

confidence: 99%

Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

Wang

Dian

2019

IEEE Access

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In recent years, the method of plant leaf classification by deep learning has gradually become mature. However, training a leaf classifier based on deep learning requires a large number of samples for supervised training. In this paper, a few-shot learning method based on the Siamese network framework is proposed to solve a leaf classification problem with a small sample size. First, the features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. 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. Finally, a k-nearest neighbor (kNN) classifier is used to classify leaves in the learned metric space. The average classification accuracy is used as a performance measure. The open access Flavia, Swedish and Leafsnap datasets are used to evaluate the performance of the method. The experimental results show that the proposed method can achieve a high classification accuracy with a small size of supervised samples.

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