Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model

Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

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Integrating Sparse and Collaborative Representation Classifications for Image Classification

International Journal of Image and Graphics ◽

10.1142/s0219467817500073 ◽

2017 ◽

Vol 17 (02) ◽

pp. 1750007 ◽

Cited By ~ 8

Author(s):

Chunwei Tian ◽

Guanglu Sun ◽

Qi Zhang ◽

Weibing Wang ◽

Teng Chen ◽

...

Keyword(s):

Sparse Representation ◽

Image Classification ◽

Linear Combination ◽

Image Recognition ◽

Test Sample ◽

Collaborative Representation ◽

Training Samples ◽

Sparse Representation Classification ◽

The Difference ◽

Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs10121934 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1934 ◽

Cited By ~ 11

Author(s):

Bao-Di Liu ◽

Wen-Yang Xie ◽

Jie Meng ◽

Ye Li ◽

Yanjiang Wang

Keyword(s):

Remote Sensing ◽

Test Sample ◽

Remote Sensing Image ◽

Image Features ◽

Superior Performance ◽

Collaborative Representation ◽

Great Success ◽

Specific Class ◽

Remote Sensing Images ◽

Training Samples

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

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A new data classification improvement approach based on kernel clustering

Journal of Physics Conference Series ◽

10.1088/1742-6596/2082/1/012021 ◽

2021 ◽

Vol 2082 (1) ◽

pp. 012021

Author(s):

Bingsen Guo

Keyword(s):

High Performance ◽

Real Life ◽

Data Classification ◽

Classification Performance ◽

Training Set ◽

Kernel Clustering ◽

Training Samples ◽

Critical Issues ◽

Extensive Performance ◽

Query Sample

Abstract Data classification is one of the most critical issues in data mining with a large number of real-life applications. In many practical classification issues, there are various forms of anomalies in the real dataset. For example, the training set contains outliers, often enough to confuse the classifier and reduce its ability to learn from the data. In this paper, we propose a new data classification improvement approach based on kernel clustering. The proposed method can improve the classification performance by optimizing the training set. We first use the existing kernel clustering method to cluster the training set and optimize it based on the similarity between the training samples in each class and the corresponding class center. Then, the optimized reliable training set is trained to the standard classifier in the kernel space to classify each query sample. Extensive performance analysis shows that the proposed method achieves high performance, thus improving the classifier’s effectiveness.

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Drug Therapeutic-Use Class Prediction and Repurposing Using Graph Convolutional Networks

Pharmaceutics ◽

10.3390/pharmaceutics13111906 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1906

Author(s):

Mapopa Chipofya ◽

Hilal Tayara ◽

Kil To Chong

Keyword(s):

Drug Efficacy ◽

Therapeutic Use ◽

New Drugs ◽

Classification Model ◽

Class Prediction ◽

Convolutional Networks ◽

Chemical Structures ◽

Therapeutic Uses ◽

Training Samples ◽

Potential Tool

An important stage in the process of discovering new drugs is when candidate molecules are tested of their efficacy. It is reported that testing drug efficacy empirically costs billions of dollars in the drug discovery pipeline. As a mechanism of expediting this process, researchers have resorted to using computational methods to predict the action of molecules in silico. Here, we present a way of predicting the therapeutic-use class of drugs from chemical structures only using graph convolutional networks. In comparison with existing methods which use fingerprints or images as training samples, our approach has yielded better results in all metrics under consideration. In particular, validation accuracy increased from 83–88% to 86–90% for single label tasks. Similarly, the model achieved an accuracy of over 88% on new test data. Finally, our multi-label classification model made new predictions which indicated that some of the drugs could have other therapeutic uses other than those indicated in the dataset. We performed a literature-based evaluation of these predictions and found evidence that validates them. This renders the model a potential tool to be used in search of drugs that are candidates for repurposing.

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Identifying Fentanyl with Mass Spectral Libraries

10.26434/chemrxiv.14176952.v1 ◽

2021 ◽

Author(s):

Anthony J. Kearsley ◽

Arun Moorthy

Keyword(s):

Law Enforcement ◽

Similarity Measures ◽

Mass Spectral Library ◽

Spectral Library ◽

Mass Spectral ◽

Fentanyl Analogs ◽

Spectral Libraries ◽

Mass Spectral Libraries

<div> <div> <div> <p>Synthesis, distribution and abuse of fentanyl, a synthetic opioid, has led to a critical worldwide epidemic. Mass spectral library searching for opioids remains unresolved despite being central to law-enforcement involving identification, monitoring and prosecution of opioid related crimes. In this article, two model problems are presented to illustrate difficulties associated with fentanyl identification. A collection of both currently-employed similarity measures and intuitive measures of dissimilarity are employed to simulate identifying fentanyl analogs with mass spectral library searching. </p> </div> </div> </div>

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Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

Scientific Programming ◽

10.1155/2020/8883214 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Jinchao Zhao ◽

Yihan Wang ◽

Qiuwen Zhang

Keyword(s):

Deep Learning ◽

Video Coding ◽

High Efficiency ◽

Texture Classification ◽

Rate Distortion ◽

Classification Model ◽

High Efficiency Video Coding ◽

Fast Encoding ◽

Training Samples ◽

Coding Unit

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

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Application of convolutional neural networks for stellar spectral classification

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz3100 ◽

2019 ◽

Vol 491 (2) ◽

pp. 2280-2300 ◽

Cited By ~ 6

Author(s):

Kaushal Sharma ◽

Ajit Kembhavi ◽

Aniruddha Kembhavi ◽

T Sivarani ◽

Sheelu Abraham ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Spectral Classification ◽

Stellar Spectra ◽

The Past ◽

Training Samples ◽

Optical Region ◽

Spectral Libraries ◽

Improved Accuracy

ABSTRACT Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR > 20.

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Graph Constraint and Collaborative Representation Classifier Steered Discriminative Projection with Applications for the Early Identification of Cucumber Diseases

Sensors ◽

10.3390/s20041217 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1217 ◽

Cited By ~ 1

Author(s):

Yuhua Li ◽

Fengjie Wang ◽

Ye Sun ◽

Yingxu Wang

Keyword(s):

Early Identification ◽

Early Stage ◽

Dimensional Space ◽

Decision Criterion ◽

Hyperspectral Data ◽

Identification Accuracy ◽

Collaborative Representation ◽

Efficient Production ◽

Spectral Curves ◽

Low Dimensional

Accurate, rapid and non-destructive disease identification in the early stage of infection is essential to ensure the safe and efficient production of greenhouse cucumbers. Nevertheless, the effectiveness of most existing methods relies on the disease already exhibiting obvious symptoms in the middle to late stages of infection. Therefore, this paper presents an early identification method for cucumber diseases based on the techniques of hyperspectral imaging and machine learning, which consists of two procedures. First, reconstruction fidelity terms and graph constraints are constructed based on the decision criterion of the collaborative representation classifier and the desired spatial distribution of spectral curves (391 to 1044 nm) respectively. The former constrains the same-class and different-class reconstruction residuals while the latter constrains the weighted distances between spectral curves. They are further fused to steer the design of an offline algorithm. The algorithm aims to train a linear discriminative projection to transform the original spectral curves into a low dimensional space, where the projected spectral curves of different diseases own better separation trends. Then, the collaborative representation classifier is utilized to achieve online early diagnosis. Five experiments were performed on the hyperspectral data collected in the early infection stage of cucumber anthracnose and Corynespora cassiicola diseases. Experimental results demonstrated that the proposed method was feasible and effective, providing a maximal identification accuracy of 98.2% and an average online identification time of 0.65 ms. The proposed method has a promising future in practical production due to its high diagnostic accuracy and short diagnosis time.

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A Novel Autonomous Perceptron Model for Pattern Classification Applications

Entropy ◽

10.3390/e21080763 ◽

2019 ◽

Vol 21 (8) ◽

pp. 763 ◽

Cited By ~ 12

Author(s):

Alaa Sagheer ◽

Mohammed Zidan ◽

Mohammed M. Abdelsamea

Keyword(s):

Pattern Classification ◽

Data Science ◽

Science Research ◽

Efficient Solutions ◽

Classification Model ◽

Computational Time ◽

Quantum Bit ◽

Training Samples ◽

Artificial Neural Network Ann ◽

Definition Of

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.

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Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142056008x ◽

2020 ◽

Vol 34 (12) ◽

pp. 2056008

Author(s):

Shuhuan Zhao

Keyword(s):

Face Recognition ◽

Sparse Representation ◽

Real Life ◽

Test Sample ◽

Single Sample ◽

Collaborative Representation ◽

Adaptive Variation ◽

Training Set ◽

Probabilistic Estimation ◽

Training Samples

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost.

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