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Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

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Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor. / Gu, Xiaowei; Angelov, Plamen Parvanov.

2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. 8851838.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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@inproceedings{bd168f1f22d74cb8be96e6851aebb258,
title = "Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor",
abstract = "In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks as the feature descriptor, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF…THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
note = "{\circledC}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2019",
month = "9",
day = "30",
doi = "10.1109/IJCNN.2019.8851838",
language = "English",
isbn = "9781728119861",
booktitle = "2019 International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/9/30

Y1 - 2019/9/30

N2 - In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks as the feature descriptor, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF…THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.

AB - In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks as the feature descriptor, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF…THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.

U2 - 10.1109/IJCNN.2019.8851838

DO - 10.1109/IJCNN.2019.8851838

M3 - Conference contribution/Paper

SN - 9781728119861

BT - 2019 International Joint Conference on Neural Networks (IJCNN)

PB - IEEE

ER -