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Research papers on Neural networks for image recognition

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  2. Convolution neural network in precision agriculture for plant image recognition and classification

    Halimatu Sadiyah Abdullahi, Ray E. Sheriff, F. Mahieddine · 2017 · International Conference on Innovative Computing Technology · 130 citations

    Agriculture is essential to the continued existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Precision Agriculture is thought to be solution required to achieve the production rate required. There has been a significant improvement in the area of image processing and data processing which has being a major challenge previously in the practice of precision Agriculture. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for diff

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  5. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Liangji Zhou, Qingwu Li, Guanying Huo, et al. · 2017 · Computational Intelligence and Neuroscience · 50 citations

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefo

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  6. A Survey on Image Classification and Activity Recognition using Deep Convolutional Neural Network Architecture

    M. Sornam, Kavitha Muthusubash, V. Vanitha · 2017 · International Conference on Advanced Computing · 47 citations

    Deep learning, over a decade it becomes the booming field for researchers since the technique has the capability to overcome the drawbacks of already used traditional algorithms which is dependent on hand designed features. Currently four different type of architecture used in deep learning which is an Autoencoder, Deep Belief Network, Convolutional Neural Network and Restricted Boltzmann Machine. According to the reported research, Convolutional Neural Network is very efficient on image classification and speech recognition. The main aim of this survey is to broadly cover the applications of convolutional networks in the field of computer-aided diagnosis for the dreadful diseases and also i

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  9. Image recognition using convolutional neural networks for classification of honey bee subspecies

    Dario De Nart, Cecília Costa, Gennaro Di Prisco, et al. · 2022 · Apidologie · 29 citations

    Abstract Four models based on convolutional neural networks were used to investigate whether image recognition techniques applied to honey bee wings could be used to discriminate among honey bee subspecies. A dataset consisting of 9887 wing images belonging to 7 subspecies and one hybrid was analysed with ResNet 50, MobileNet V2, Inception Net V3, and Inception ResNet V2. Accuracy values of classification of individual wings were over 0.92, and all models outperformed traditional morphometric evaluation. The Inception models achieved the highest accuracies and higher scores of precision and recall for most classes. When wing images were grouped by colony, almost all wings in the colony sampl

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  10. High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network

    Zhizhe Liu, Luo Sun, Qian Zhang · 2022 · Computational Intelligence and Neuroscience · 18 citations

    Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different par

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  12. Image Classification and Recognition of Medicinal Plants Based on Convolutional Neural Network

    Yang Liu, Yating Li, Yanjie Zhao, et al. · 2021 · IEEE International Conference on Communication Technology (ICCT) · 13 citations

    With the development of computer vision, plant identification systems have helped botanists recognize and identify plant species more quickly. We usually differentiate plants of different species based on morphology, texture, color, and other characteristics. These features can exhibit high degrees of similarity, and the differences between species can be minor. This paper proposes a classification and recognition method for medicinal plants based on the ResNet34 convolutional neural network, focusing on the image recognition of medicinal grassland plants. First, we use targeted and effective classifications for species tasks based on the Plant-Net. Then, weakly supervised attention function

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  13. Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

    Maissa HAMOUDA, Med Salim BOUHLEL · 2025 · IET Image Processing · 7 citations

    Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN‐HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN‐HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra‐convolution technique (ExCNN) is proposed, where we add layers of global convolutions on the classified images, o

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  14. Research on Classification and Recognition of Object Image Based on Convolutional Neural Network

    Xianlun Wang, Zuomin Yang · 2020 · IOP Conference Series Materials Science and Engineering · 4 citations

    Abstract Image recognition and classification using a convolution neural network is an important application of image processing. How too reasonably to design the convolutional layer of the convolutional neural network, the number of hidden layers and optimize the parameters of the convolutional neural network to ensure high accuracy and efficiency in image recognition and classification are an extremely important part. The core of this thesis is the model design and parameter optimization of deep convolutional neural networks. This paper mainly designs, implements, optimizes and adjusts the model structure of the convolutional network of Tensor Flow framework platform. We redesigned the con

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  15. Creating Deep Convolutional Neural Networks for Image Classification

    Nabeel Siddiqui · 2023 · Programming Historian · 2 citations

    This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google's Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.

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  16. Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

    Taoyu Liu · 2024 · Applied and Computational Engineering · 2 citations

    This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse co

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  18. Convolutional Neural Networks and Pattern Recognition: Application to Image Classification

    Christy Ntambwe Kabamba, Lucie Mpuekela .N, Simon Ntumba .B, et al. · 2019 · Zenodo (CERN European Organization for Nuclear Research)

    This research study focuses on pattern recognition using convolutional neural network. Deep neural network has been choosing as the best option for the training process because it produced a high percentage of accuracy. We designed different architectures of convolutional neural network in order to find the one with high accuracy of image classification and optimum bias. We used CIFAR-10 data set that contains 60000 Images to train our model on architectures. The best architecture was able to classify images with 95.55% of accuracy and an error of 0.32% using cross validation method. We note that, the numbers of epoch while running the model and the depth of the architecture are factors that

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  19. Image Classification And Recognition Based On The Deep Convolutional Neural Network

    Yuanyuan Wang, Longjun Zhang, Xiao Yang, et al. · 2017 · Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC)

    With the development of the information age, there were a lot of data whose features couldn't be extracted or predicted effectively in real life.One of the core function of computer vision technology is to classify and recognize, with classification and recognition as its summit mission of object detection and object positioning.Due to image data were affected by multiple factors such as illumination, environment, angle, certain object features couldn't be established by manual coding, and it is hard for high latitude data in a computer to realize real-time object detection, object localization, classification and recognition.Therefore the higher accuracy in classification could be obtained

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  20. Improvement of convolutional neural networks in image classification and recognition

    Pengju Xia · 2025 · International Journal of Innovative Computing and Applications

    Image classification, a vital deep learning technique in vision, has been widely applied in fields such as agricultural detection. However, traditional methods often suffer from low efficiency and weak analytical capability. To address this, we propose a CNN-ELM based image classification model incorporating a feature reorganisation attention mechanism and capsule networks for extracting complex semantic information. Experimental results show that when the iteration number of the fusion algorithm reaches 200, the average loss value is 0.026, significantly lower than that of the transfer learning ensemble algorithm (0.082) and the FPGA algorithm (0.087). Moreover, the hybrid model achieves a

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