Image Classification Algorithm Based on Deep Learning-Kernel Function

Liu, June; F, An

doi:10.1155/2020/7607612

Cited by 55 publications

(30 citation statements)

References 45 publications

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“…As it is shown in Fig. 7, almost all 2-D entropies of images in the original dataset distribute in the interval [6,12]. To derive 10 subdatasets from the original dataset, we partition the interval into 10 segments with equal widths at first.…”

Section: Experiments and Analyses A Experiments Designmentioning

confidence: 99%

“…Conventional machine learning based approaches usually need to extract features offline beforehand, such as those based on SVM, KNN, Regression and so on [4]. In recent years, due to the explosive growth of image data on the Internet, deep learning has been widely used in image classification [5], [6]. For example, deep CNN based approaches to detecting DR are proposed in [7], [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

Chen

Yang

et al. 2020

IEEE Access

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The early detection of Diabetic Retinopathy (DR) is critical for diabetics to lower the blindness risks. Many studies represent that Deep Convolutional Neural Network (CNN) based approaches are effective to enable automatic DR detection through classifying retinal images of patients. Such approaches usually depend on a very large dataset composed of retinal images with predefined classification labels to support their CNN training. However, in some occasions, it is not so easy to get enough welllabelled images to act as model training samples. At the same time, when a CNN becomes deeper, its training will not only take much longer time, but also be more likely to lead to overfitting, especially on a large training dataset. Therefore, it is meaningful to explore a simpler CNN based approach that is still effective on small datasets to classify retinal images. In this paper, an approach to retinal image classification is proposed based on the integration of multi-scale shallow CNNs. Experiments on public datasets show that, on small datasets, the proposed approach can improve the classification accuracy by 3% compared with current representative integrated CNN learning approaches. On the bigger dataset, the proposed approach can improve the classification accuracy by 3% to 9% compared with other representative approaches such as traditional CNN, LCNN and VGG16noFC. The evaluation also represents that, though the classification accuracy of the proposed approach declines by 6% on the smallest dataset containing only 10% samples of the original dataset, its time cost declines to about 30% of that on the original dataset. INDEX TERMS convolutional neural network, diabetic retinopathy, image classification, integrated learning, performance integration.

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Section: Experiments and Analyses A Experiments Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

Chen

Yang

et al. 2020

IEEE Access

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“…Convolutional neural network (CNN) has been proved as an effective tool for image restoration [1][2][3][4][5]. Recently, there are CNN-based works for SISR problem [19,20].…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning has shown its amazing performance in various tasks [1][2][3][4][5]. Nowadays, there are convolutional neural network-(CNN-) based works focusing on SISR problem.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Cao

Liu

Duan

et al. 2020

Scientific Programming

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Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

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“…However, the scope of the method is to classify general scene images but not water images. Liu et al [10] explored a deep learning kernel function for image classification. The main ideas of the method are to use sparse representation to design a deep learning network.…”

Section: Review Of Related Workmentioning

confidence: 99%

Deep invariant texture features for water image classification

Xue

Shivakumara

et al. 2020

SN Appl. Sci.

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Detecting potential issues in naturally captured images of water is a challenging task due to visual similarities between clean and polluted water, as well as causes posed by image acquisition with different camera angles and placements. This paper presents novel deep invariant texture features along with a deep network for detecting clean and polluted water images. The proposed method first divides an input image into H, S and V components to extract finer details. For each of the color spaces, the proposed approach generates two directional coherence images based on Eigen value analysis and gradient distribution, which results in enhanced images. Then the proposed method extracts scale invariant gradient orientations based on Gaussian first order derivative filters on different standard deviations to study texture of each smoothed image. To strengthen the above features, we explore the combination of Gabor-wavelet-binary pattern for extracting texture of the input water image. The proposed method integrates merits of aforementioned features and the features extracted by VGG16 deep learning model to obtain a single feature vector. Furthermore, the extracted feature is fed to a gradient boosting decision tree for water image detection. A variety of experimental results on a large dataset containing different types of clean and stagnant water images show that the proposed method outperforms the existing methods in terms of classification rate and accuracy.

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Image Classification Algorithm Based on Deep Learning-Kernel Function

Cited by 55 publications

References 45 publications

An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

An Approach to Detecting Diabetic Retinopathy Based on Integrated Shallow Convolutional Neural Networks

Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Deep invariant texture features for water image classification

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