Very high resolution images classification by fine tuning deep convolutional neural networks

Iftene, Meziane; Liu, Q.; Wang, Y.

doi:10.1117/12.2244339

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…The results obtained for the three combinations of CaffeNet and GoogleNet as features extractors from the last convolutional layer (C5) and the output of the top inception module (IM) respectively, are reported for WHU-RS dataset in the following tables where they are compared to the results obtained for the same dataset classification task by fine tuning each CNN model [10] and CaffeNet model as features extractor from fully connected layer and convolutional layer [3]. Tables 1 and 2 display the WHU-RS dataset classification accuracies of our different fusion models compared with the previous work done on WHU-RS dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The collection from different resolution satellite images, the variation in scale, illumination and viewpoint-dependent appearance in some categories make this dataset more complicated [3] as displayed in Figure 2. The results obtained are evaluated by comparing them to those obtained by [10], where authors fine-tuned the CNN models, and [3] where the CNNs are considered as features extractors; both works were carried out for the classification of the WHU-RS dataset.…”

Section: Proposed Approachmentioning

confidence: 99%

“…al [8], is to fine-tune the CNN parameters using the target dataset after that parameters are learned on a large dataset with data diversity such as ImageNet database [9]; this approach is called Transfer Learning (TL) and the parameters transfer success depends on the similarity of the target dataset to the ImageNet database. Taking this into account in the previous work [10], we evaluate the generalization of deep convolutional neural networks features from the last fully-connected layer in the classification process of a small dataset of a VHR imagery by finetuning CNN deeper layers only. The obtained results show better accuracy compared to that of CNNs as features extractor.…”

Section: Introductionmentioning

confidence: 99%

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Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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Abstract.Recently, deep convolutional neural networks (CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution (VHR) images classification task which is a key point in the remote sensing field. This work aims to improve the VHR image classification accuracy by exploiting the fusion of two pre-trained deep convolutional neural network models. In this paper, we propose to concatenate the features extracted from the last convolutional layer of each pre-trained deep convolutional neural network to get a long features vector which is fed into a fullyconnected layer and then perform a fine-tuning for a VHR image dataset classification. The experimental results are promising since they show that the fusion of two deep CNNs achieves better accuracy for the classification compared to the individual CNN models on the same dataset.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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“…These operators compute the maximum or the average value within a small spatial block [27]. Pooling with filters size of 2 × 2 with a stride of 2 are considered ideal [29]. Fig.…”

Section: ̂= ( ) (2)mentioning

confidence: 99%

“…Fully-connected layer connects to all the neurons of the previous layer (Fig. 2) [29]. Fully connected layers are typically used as last layer of the network and perform the classification.…”

Section: ̂= ( ) (2)mentioning

confidence: 99%

Deep Tweets Analyzer Model for Twitter Mood Visualization and Prediction Based Deep Learning Approach

Al-Ghalibi¹,

Al-Azzawi²,

Lawonn³

2019

IJCCE

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In many of today's big data analytics applications, it might need to analyze social media feeds as well as to visualize users' opinions. This will provide a viable alternative source to establish new metrics in our digital life. Social interaction with people in Twitter is open-ended, making media analysis in Twitter easier in comparison with other social media. That is because the interaction in those media is often different since most of them are private. This work is therefore devoted to focus merely on design and implementation a Deep model for Twitter opinion (Mood) visualization based Deep Learning network. It is concerned with Natural Language Processing (NLP)-based sentiment analysis and Deep Learning framework for Twitter's opinion mining visualization and classification. The utilized methodology is based on applying sentiment analysis NLP on a large number of tweets in order to visualize the predicted mood scoring of the tweet and thus to exploit public tweeting for knowledge discovery. This will moreover serve for fake news detection. The pertinent mechanism involves several consecutive steps, namely: dataset collection stage, the pre-processing stage, NLP stage, sentiment analysis stage, and prediction and classification stage using Deep Learning Model. The U.S. Airlines Sentiment Analysis Twitter dataset has been utilized which is already provided with Data for Everyone. The presented system is monitoring Twitter streams from both the media and the public. It is capable to visualize and extract meaningful data from tweets in real-time and store them into a Deep model for analysis. It is convenient for a wide application spectrum involving: big data analytics solutions, predicting e-commerce customer's behavior, improving marketing strategy, getting market competitive advantages, besides visualization in various data mining applications.

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Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

Liu

Weng

2019

Health Informatics

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Very high resolution images classification by fine tuning deep convolutional neural networks

Cited by 9 publications

References 6 publications

Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

Deep Tweets Analyzer Model for Twitter Mood Visualization and Prediction Based Deep Learning Approach

Advancing Clinical Research Through Natural Language Processing on Electronic Health Records: Traditional Machine Learning Meets Deep Learning

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