Convolutional neural networks for image classification

Jmour, Nadia; Zayen, Sehla; Abdelkrim, Afef

doi:10.1109/aset.2018.8379889

Cited by 180 publications

(66 citation statements)

References 18 publications

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“…(2) CNN Pooling Layer The next operation (pooling) aims to merge similar features (values that statistically are highly correlated) into one, and this task is usually done by choosing the maximum value in a local patch for the output [41]. The pooling layer (downsampling operation) attempts to reduce the spatial size of the output and it controls overfitting, as well.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…(3) CNN Fully Dense Layer In the end, the Softmax classifier (activation function) was used to predict the exclusive classes and the characteristics of the CNN code were combined into a fully dense layer to classify each pixel in the image to the most likely label [18,41]. Softmax classifier discriminates the category of each pixel by weighting the distance between validation data and training datasets from that class [43].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

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Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)-digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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“…Its architecture is described in Fig.5 [6]. Based on this approach, we reached a classification accuracy of 93.33% [31]. In the current work, our overarching approach is to design our own CNN model for stop sign image classification.…”

Section: Problem Statementmentioning

confidence: 98%

Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms

Loussaief¹,

Abdelkrim²

2018

ijacsa

Self Cite

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In machine learning for computer vision based applications, Convolutional Neural Network (CNN) is the most widely used technique for image classification. Despite these deep neural networks efficiency, choosing their optimal architecture for a given task remains an open problem. In fact, CNNs performance depends on many hyper-parameters namely CNN depth, convolutional layer number, filters number and their respective sizes. Many CNN structures have been manually designed by researchers and then evaluated to verify their efficiency. In this paper, our contribution is to propose an innovative approach, labeled Enhanced Elite CNN Model Propagation (Enhanced E-CNN-MP), to automatically learn the optimal structure of a CNN. To traverse the large search space of candidate solutions our approach is based on Genetic Algorithms (GA). These meta-heuristic algorithms are wellknown for non-deterministic problem resolution. Simulations demonstrate the ability of the designed approach to compute optimal CNN hyper-parameters in a given classification task. Classification accuracy of the designed CNN based on Enhanced E-CNN-MP method, exceed that of public CNN even with the use of the Transfer Learning technique. Our contribution advances the current state by offering to scientists, regardless of their field of research, the ability of designing optimal CNNs for any particular classification problem.

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“…Third, the classification of the image is completed by Support Vector Machine (SVM). The position with the selective search is that no learning is done, and it takes a long time in training to identify the regions [20,21].…”

Section: Region Proposal Based Algorithmsmentioning

confidence: 99%

Object Detection in Convolution Neural Networks Using Iterative Refinements

Aroulanandam¹,

Latchoumi²,

Bhavya³

et al. 2019

RIA

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One of the major problems in computer vision is the object detection through image classification. In recent years, the convolutional neural network (CNN) has been extensively applied for image classification. This paper combines iterative refinement and joint score function into a strategy to accurately localize the detected objects. First, a unified model with fast approximation was proposed to correct the position and range of region proposals, which are often incorrect in conventional linear methods. Focusing on data, the model can acquire knowledge without any cost, and suit different CNN architectures for various datasets. Next, a joint score function was introduced to process the number of candidate regions in the images. The joint score function deals with the relative position of the occluded object, and depends on the image data and output loss. Experimental results show that the proposed strategy achieved a 3.6% higher mean precision than the contrastive method. The research greatly promotes the object detection accuracy in computer vision.

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Convolutional neural networks for image classification

Cited by 180 publications

References 18 publications

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Convolutional Neural Network Hyper-Parameters Optimization based on Genetic Algorithms

Object Detection in Convolution Neural Networks Using Iterative Refinements

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