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

Gu, Xiaowei, Angelov, Plamen P. (2019) Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor. In: Neural Networks (IJCNN), The 2013 International Joint Conference. 2019 International Joint Conference on Neural Networks (IJCNN). . pp. 1-7. IEEE ISBN 978-1-72811-986-1. E-ISBN 978-1-72811-985-4. (doi:10.1109/IJCNN.2019.8851838) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90195)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/IJCNN.2019.8851838

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 for feature extraction, 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.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/IJCNN.2019.8851838
Uncontrolled keywords: Image segmentation; Feature extraction; Prototypes; Semantics; Image analysis; Visualization; Mathematical model; deep rule-based; deep convolutional neural network; ensemble feature descriptor; aerial scene classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 09:41 UTC
Last Modified: 15 Sep 2021 16:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90195 (The current URI for this page, for reference purposes)
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