Image Classification Algorithm Research Articles

Making a Better Logistic Regression Model Using Ensemble Learning

Abstract: Machine learning is a hot research topic in today's society and an important direction in the field of image classification research. Image classification is a supervised learning method used to classify images. There are many image classification algorithms that perform differently in different conditions. Paper focuses on improving the performance of image classification algorithms by using ensemble learning. Paper analyses the performance of logistic regression model and support vector machine model and then uses ensemble learning to increase the performance of logistic regression model. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of three algorithms when tested on same dataset, in same environment and on same system. The final results of the research prove that ensemble techniques give best results and this type of learning is effective.

Hyperspectral Image Classification Based on Two-Branch Multiscale Spatial Spectral Feature Fusion with Self-Attention Mechanisms

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

Research on efficient classification algorithm for coal and gangue based on improved MobilenetV3-small

ABSTRACT Aiming at the problems of insufficient attention to spatial information, long classification training time, and high model complexity in current gangue image classification algorithms, a lightweight fast recognition model for gangue based on improved MobileNetV3-small is proposed. First, we optimize the feature extraction part by introducing the CeLU activation function to alleviate the neuron death and gradient vanishing problems, thus improving the performance of the model. Second, Bneck is further optimized using an improved Triplet Attention Module to achieve almost parameter-free spatial and channel dimension interactions, which drastically reduces the complexity of the model. Finally, we construct a new Efficient Last Stage structure, which improves the model performance while successfully reducing the computation and model size by replacing the traditional convolution with Ghost Module and introducing Mix-pool as a pooling layer. In addition, the effectiveness of each component was fully demonstrated through ablation experiments, visualization analysis, and comparative experiments. The experimental results show that training the homemade gangue dataset using the improved MobilenetV3-small model improves Accuracy and F1-Score on the validation set by 0.48% and 0.42%, respectively, compared to the original network, and in terms of model complexity, we can see that FLOPs, number of parameters, and Weight size are reduced by 2.8%, 33.0%, and 32.3%, respectively, and FPS reaches 28 frames per second. The improved algorithm greatly reduces the model complexity and decreases the dependence on hardware devices while improving the classification accuracy and anti-interference capability. This provides an important reference basis for deploying automated underground coal gangue sorting, which helps to realize intelligent integrated mining.

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

AI-based image classification algorithms for Leukaemia diagnostics and hematologic cytomorphology: From single cells to molecular features

Due to the progress of image analysis and classification systems in recent years, algorithms have been developed that support morphologic examination of both single cells and tissue samples. These algorithms are typically developed using data-driven strategies, which require comprehensive, large-scale datasets. In the diagnostic workup of hematopoietic malignancies, cytomorphologic examination and differentiation represents a key first step. In recent years, the availability of large-scale, high-quality datasets of single leukocytes from peripheral blood and bone marrow has led to the development of diagnostic support algorithms for this modality. These methods not only allow a faster and more consistent classification of diagnostically relevant cell types, but also pave the way for integrated analysis of cytomorphologic and molecular findings.

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

Detection and classification of wilting status in leaf images based on VGG16 with EfficientNet V3 algorithm

The aim of this paper is to explore the importance of leaf wilting status detection and classification in agriculture to meet the demand for monitoring and diagnosing plant growth conditions. By comparing the performance of the traditional VGG16 image classification algorithm and the popular EfficientNet V3 algorithm in leaf image wilting status detection and classification, it is found that EfficientNet V3 has faster convergence speed and higher accuracy. As the model training process proceeds, both algorithms show a trend of gradual convergence of Loss and Accuracy and increasing accuracy. The best training results show that VGG16 reaches a minimum loss of 0.288 and a maximum accuracy of 96% at the 19th epoch, while EfficientNet V3 reaches a minimum loss of 0.331 and a maximum accuracy of 97.5% at the 20th epoch. These findings reveal that EfficientNet V3 has a better performance in leaf wilting status detection, which provides a more accurate and efficient means of plant health monitoring for agricultural production and is of great research significance.

Houses classification using vision transformer with shifted patch tokenization

Deep learning has recently made great progress leading to revolutionizing image recognition, speech recognition, and natural language processing tasks that were previously challenging to make using traditional techniques. Image classification offers a lot of potential for architectural design, even though it is rarely used to uncover new techniques. It can be used to determine the client’s preferences and design a building that satisfies those preferences. The different architectural styles based on culture, region, and time are one of the main challenges for image classification in architecture. Hence, it can be challenging for untrained clients to recognize an architectural style, and sometimes some buildings are made up of various types that are difficult to classify as a single style. This paper investigates the potential of employing state-of-art cutting-edge image classification algorithms in houses classification. In addition, the paper proposes the uses of Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) in order to enhance the performance of Vision transformer (ViT) when trained to classify house images with a small dataset, opposed to the regular ViT which requires huge dataset in order to converge. Experimentally, these techniques proved to have a positive impact on the performance of the ViT, which reached 96.85% accuracy when SPT and LSA are employed.

An efficient densenet-based deep learning model for Big-4 snake species classification

Snakebite poses a significant health threat in numerous tropical and subtropical nations, with around 5.4 million cases reported annually, which results in 1.8–2.7 million instances of envenomation, underscoring its critical impact on public health. The 'BIG FOUR' group comprises the primary committers responsible for most snake bites in India. Effective management of snakebite victims is essential for prognosis, emphasizing the need for preventive measures to limit snakebite-related deaths.The proposed initiative seeks to develop a transfer learning-based image classification algorithm using DenseNet to identify venomous and non-venomous snakes automatically. The study comprehensively evaluates the image classification results, employing accuracy, F1-score, Recall, and Precision metrics. DenseNet emerges as a potent tool for multiclass snake image classification, achieving a notable accuracy rate of 86%. The proposed algorithm intends to be incorporated into an AI-based snake-trapping device with artificial prey made with tungsten wire and vibration motors to mimic heat and vibration signatures, enhancing its appeal to snakes.The proposed algorithm in this research holds promise as a primary tool for preventing snake bites globally, offering a path toward automated snake capture without human intervention. These findings are significant in preventing snake bites and advancing snakebite mitigation strategies.

A Copula Based Unsupervised Domain Adaptation for Image Classification

In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze separately the dependency structure and the marginals by Sklar’s theorem. In particular, we propose to align the Gaussian copula correlations in the copula feature spaces instead of aligning the correlation matrices in the original space. Considering the extremely skewed distribution of SURF image features in Office-Caltech10 data set we apply PCA first in order to extract some skewness-mitigated principal features and then derive copula features to align for domain adaptation by using CORAL idea with Gaussian copula correlation matrices. The proposed method showed a good classification accuracy when applied to image classification problem in an unsupervised domain adaptation setting.

Study on intelligent assembly process planning and execution system based on digital twin

Purpose The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality. Design/methodology/approach Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality. Findings Digital twin technology enhances the quality and efficiency of assembly process planning and execution system. Originality/value It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Review of Convolutional Neural Network Models and Image Classification

With the arrival of the era of big data and the improvement of computing power, deep learning has swept the world. Traditional image classification methods are difficult to deal with the huge image data, and cannot meet the requirements of people on the accuracy and speed of image classification, the image classification method based on convolutional neural network breaks through the bottleneck of the traditional image classification method, and becomes the mainstream algorithm of image classification, how to effectively use convolutional neural network to classify images has become a hot spot of research in the field of computer vision at home and abroad. In this paper, we review the research background, significance and current research status of convolutional neural network model and image classification, study two image classification methods based on ResNet and ShuffleNet, and provide a comprehensive review of the construction methods and characteristics of the two deep convolutional neural network model structures, and finally compare and analyse the performance of the two classification models.

Detecting Halyomorpha halys using a low-power edge-based monitoring system

Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.

Application research of image classification algorithm based on deep learning in household garbage sorting

The classification of garbage types is an important issue in today's world, and its proper implementation can contribute to environmental conservation and improved efficiency of recycling processes. Unfortunately, the classification of garbage types is currently predominantly performed through human supervision, which leads to high errors and environmental risks. It is crucial to automate this procedure utilizing machine vision methods as a result. This research proposes a revolutionary deep learning-based strategy for classifying domestic waste. The suggested method uses deep learning methods to extract information from images. The Capuchin Search Algorithm (CapSA) is used to improve the hyperparameters of the convolutional neural network (CNN) used as the feature extraction model. Furthermore, for categorizing the retrieved features from the CNN model, a hybrid learning model based on Error-Correcting Output Codes (ECOC) and Artificial Neural Networks (ANN) is used. The classification accuracy may be successfully increased by using this hybrid model, and the benefit becomes more pronounced as the number of target categories rises. The TrashNet and HGCD databases were used to assess the suggested method's effectiveness, and its results in waste type detection were contrasted with those of earlier techniques. Based on the study findings, the suggested approach can identify trash types with an accuracy of 98.81 % and 99.01 % on the TrashNet and HGCD databases, respectively. This is at least a 1.46 % improvement over earlier approaches. The study's conclusions validate that the suggested strategy may be used in real-world scenarios and show how successful the approaches used in it are.

Generic Framework for Vehicle Identification System with Deep Learning Models

In contemporary vehicle scenario, different kinds of vehicles are playing vital role for the customers. Day by day, vehicles are increasing with different properties and identifications. Monitoring and maintain the records of vehicles in digital platform is the challenging task as well as a crucial task for the countries. Here, we have to take the scenario of digitalization of vehicles with deep learning network models for identification and maneuvering process. In our country, different states are following multiple vehicle identification like numbering, coloring and imaging. Many researchers were contributing towards this identification mechanism like supervised, unsupervised models for getting optimum accuracy. Text and Image classification are not sufficient to solve this vehicle identification problem. We are incorporating text, image and video related unstructured data set to solve this problem. We have framed a generic architecture for all kind of vehicles analyze the data. For instance, a vehicle is moving towards CCTV camera and it has been recorded as video content. In this paper, we come across video to still pictures and image segmentation and all the identification parameters like White, Yellow and green. We performed both text classification and image classification algorithms to acquire effective result compared to existing algorithms. Bi Directional RNN (LSTM/GRU) and LeNet, Alex Net, Inception, VggNet and ResNet algorithms are analyzed for this entire process. This is one of the globally challenging scenarios for identification with feature extraction, data cleaning and processing. At the end of the result, we achieved 98.94 % accuracy compare than other existing systems.

Elaboration of the Land use Land Cover Plan of the Subdivision of Monatele in Cameroon

Over the past years, humans have directly or indirectly affected the Earth’s surface through various activities. These changes in terrestrial ecosystems are closely linked with the issue of the sustainability of socio- economic development since they affect essential parts of our natural capital such as climate, soils, vegetation, water resources and biodiversity. Land Cover has undergone several changes in Cameroon since its independence in 1960. One of the major drivers of this change is urbanization. Uncontrolled, rapid urbanization has had many negative impacts on the center region of Cameroon, around the continuum of its headquarter and in Cameroon metropolises such as anarchical constructions, pollution and traffic congestions. Faced with these urbanization issues, planners need to know the spatiotemporal trends of Land Use Land Cover for the past, present and have predictions of possible future patterns in order to better orient their planning. This is possible through the establishment of Land Use Land Cover maps. Remote sensing and Geographic Information Systems represent a cost-effective method for accomplishing this. In this work, we examine the case of Monatélé, a growing city around the Yaoundé metropolis. Satellite images from 2010, 2017, 2022 were downloaded and classified into five classes using the Support Vector Machine algorithm of image classification. It was found that between 2010 and 2022, settlements have increase continuously from 1.61% to 4.28%, water has decreased continuously from 9.81% to 6.72%, forest has experienced a net decrease from 73.77% to 68.25%, agriculture has experienced a net increase from 12.44% to 16.92%, while Bare Land has had a net increase from 2.38% to 3.82%. of the total surface area of the subdivision of Monatélé. Predictions made for 2029 and 2035 show that by 2035, settlements would have increased to 6.58%, water would have decreased to 4.58%, forest would have decreased to 61.22%, agriculture will take 25.19%, and bare land would have almost remained unvaried, taking up 2.43% of the surface area from 2010 to 2035. This will represent for Monatélé an increase in anarchical settlements, an increased loss in biodiversity, an increased pollution, an increase in demand for basic commodities, and an increased pressure on natural resources, if no planning measures are put in place, though it may represent an increase in human capital, an increase in local employment opportunities, a reduction of groceries expenditures.

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee

BackgroundTraumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies. ObjectiveTo develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures. MethodsThis retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience. ResultsAnalysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs. ConclusionThe AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.

Classification of Mango Leaf Disease using Machine Learning to Enhance Yield Productivity

Fruits provide critical nourishment to the human body. Proper Care and upkeep are essential for the fruit to be healthy. Lack of maintenance, infections, spot, fungus all cause significant production and profit losses. Mango is a seasonal and famous fruit which is consumed all over the world. It is a delicate fruit that is susceptible to disease that reduce the quality as well as quantity. Manual illness or infection inspection is a time-consuming and labour-intensive technique that necessitates a large amount of resources and therefore it is inefficient. On the other hand, automatic inspection provides various advantages such as less time consuming, less labour and also the number of resources required are less. Image classification techniques and algorithms can be used to distinguish between infected and healthy mangoes, decreasing losses.

Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.

Efficiency measurement of FL algorithms for image classification

Federated Learning (FL) has emerged as a promising approach to collaborative machine learning without the need to share raw data. It enables decentralized model updates while preserving the privacy of each device and reducing the communication overhead. This experiment evaluates the effectiveness of the personalized FL algorithms, namely FedAvg, APPLE, FedBABU and FedProto, in a decentralized setting, with a particular focus on the Fashion MNIST dataset, which is characterized by a non-ideal data distribution. The objective is to identify which algorithm performs optimally in image classification tasks. The experimental results show that both FedProto and APPLE have nearly equivalent and better performance compared to FedBABU and FedAvg. Interestingly, increasing the number of uploads in FedBABU leads to similar results to APPLE and FedProto. However, under limited upload conditions, FedBABU performs similarly to FedAvg. These results provide valuable insights into the differential performance of personalized FL algorithms in non-id data scenarios and provide guidance for their application in distributed environments, especially in sensitive domains such as medical, military and confidential image analysis tasks where privacy and communication efficiency are paramount concerns.