Indoor/outdoor image classification using GIST image features and neural network classifiers

2015 ◽  
Author(s):  
Waleed Tahir ◽  
Aamir Majeed ◽  
Tauseef Rehman
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Image Features ◽  
Neural Network Classifiers

scholarly journals Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3587 ◽  
Author(s):  
Chenming Li ◽  
Simon X. Yang ◽  
Yao Yang ◽  
Hongmin Gao ◽  
Jia Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Infrared Imaging ◽  
Remote Sensing Image ◽  
Image Features ◽  
Step Size ◽  
Imaging Spectrometer ◽  
Remote Sensing Image Classification

In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.

scholarly journals Robustness Testing Framework For Neural Network Image Classifier

2021 ◽  
Vol 2078 (1) ◽  
pp. 012050
Author(s):  
Duo Li ◽  
Chaoqun Dong ◽  
Qianchao Liu
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Security Risks ◽  
Robustness Testing ◽  
Testing Framework ◽  
Adversarial Examples ◽  
Test Sets ◽  
Test Framework ◽  
Neural Network Classifiers ◽  
Test Process

Abstract Neural network has made remarkable achievements in the field of image classification, but they are threatened by adversarial examples in the process of application, making the robustness of neural network classifiers face danger. Programs or software based on neural network image classifiers need to undergo rigorous robustness testing before use and promotion, in order to effectively reduce losses and security risks. To comprehensively test the robustness of neural network image classifiers and standardize the test process, starting from the two aspects of generated content and interference intensity, a variety of robustness test sets are constructed, and a robustness testing framework suitable for neural network classifiers is proposed. And the feasibility and effectiveness of the test framework and method are verified by testing LENET-5 and the model reinforced by the adversavial training.

scholarly journals Smooth Non-increasing Square Spatial Extents of Filters in Convolutional Layers of CNNs for Image Classification Problems

2018 ◽  
Vol 23 (1) ◽  
pp. 52-62
Author(s):  
Vadim V. Romanuke
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Network Architecture ◽  
Network Performance ◽  
Medium Size ◽  
Classification Problems ◽  
Layered Architecture ◽  
The Neural Network ◽  
Neural Network Classifiers ◽  
Secondary Condition

Abstract The present paper considers an open problem of setting hyperparameters for convolutional neural networks aimed at image classification. Since selecting filter spatial extents for convolutional layers is a topical problem, it is approximately solved by accumulating statistics of the neural network performance. The network architecture is taken on the basis of the MNIST database experience. The eight-layered architecture having four convolutional layers is nearly best suitable for classifying small and medium size images. Image databases are formed of grayscale images whose size range is 28 × 28 to 64 × 64 by step 2. Except for the filter spatial extents, the rest of those eight layer hyperparameters are unalterable, and they are chosen scrupulously based on rules of thumb. A sequence of possible filter spatial extents is generated for each size. Then sets of four filter spatial extents producing the best performance are extracted. The rule of this extraction that allows selecting the best filter spatial extents is formalized with two conditions. Mainly, difference between maximal and minimal extents must be as minimal as possible. No unit filter spatial extent is recommended. The secondary condition is that the filter spatial extents should constitute a non-increasing set. Validation on MNIST and CIFAR- 10 databases justifies such a solution, which can be extended for building convolutional neural network classifiers of colour and larger images.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

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