scholarly journals Review of white box methods for explanations of convolutional neural networks in image classification tasks

2021 ◽  
Vol 30 (05) ◽  
Author(s):  
Meghna P. Ayyar ◽  
Jenny Benois-Pineau ◽  
Akka Zemmari
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classification Tasks

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

2017 ◽  
Vol 29 (9) ◽  
pp. 2352-2449 ◽  
Author(s):  
Waseem Rawat ◽  
Zenghui Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Rapid Progression ◽  
Deep Convolutional Neural Networks ◽  
Computing Power ◽  
Current Trends ◽  
Visual Tasks ◽  
Classification Tasks

Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

scholarly journals Deep Residual Learning for Image Classification using Cross Validation

2020 ◽  
Vol 9 (4) ◽  
pp. 1525-1530
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Present Article ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Cross Validation ◽  
Residual Learning ◽  
Classification Tasks ◽  
Residual Block

Convolutional Neural Networks (CNN) are very common now especially in the image classification tasks as CNN’s have better classification accuracy than other techniques available in image classification. Another type of CNN called as Residual Neural Networks (RESNET) are gaining popularity because of better accuracy than normal CNN because of residual block available in it. In the present article the RESNET architecture is used for image classification on CIFAR-10 dataset using cross-validation approach that reflects a consistently better accuracy on the above dataset.

scholarly journals PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chaohui Tang ◽  
Qingxin Zhu ◽  
Wenjun Wu ◽  
Wenlin Huang ◽  
Chaoqun Hong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Image Classification ◽  
Image Enhancement ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Training Samples ◽  
Enhancement Method ◽  
Deep Cnn ◽  
Classification Tasks

In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.

scholarly journals Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Liangji Zhou ◽  
Qingwu Li ◽  
Guanying Huo ◽  
Yan Zhou
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Feature Space ◽  
Limited Capacity ◽  
The Hierarchical Structure ◽  
Classification Tasks ◽  
Traditional Pattern ◽  
Deep Learning Model

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 therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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