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Cogent Confabulation based Expert System for Segmentation and Classification of Natural Landscape ImagesBRAOVIC, M.   , STIPANICEV, D. , KRSTINIC, D. |
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Author keywords
expert systems, image classification, image color analysis, image segmentation, knowledge engineering
References keywords
image(12), processing(9), vision(7), detection(7), stipanicev(6), classification(6), smoke(5), segmentation(5), jakovcevic(5), fire(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 85 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02012
Web of Science Accession Number: 000405378100012
SCOPUS ID: 85020089483
Abstract
Ever since there has been an increase in the number of automatic wildfire monitoring and surveillance systems in the last few years, natural landscape images have been of great importance. In this paper we propose an expert system for fast segmentation and classification of regions on natural landscape images that is suitable for real-time applications. We focus primarily on Mediterranean landscape images since the Mediterranean area and areas with similar climate are the ones most associated with high wildfire risk. The proposed expert system is based on cogent confabulation theory and knowledge bases that contain information about local and global features, optimal color spaces suitable for classification of certain regions, and context of each class. The obtained results indicate that the proposed expert system significantly outperforms well-known classifiers that it was compared against in both accuracy and speed, and that it is effective and efficient for real-time applications. Additionally, we present a FESB MLID dataset on which we conducted our research and that we made publicly available. |
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[1] Comments on “MLCM: Multi-Label Confusion Matrix”, Krstinić, Damir, Šerić, Ljiljana, Slapničar, Ivan, IEEE Access, ISSN 2169-3536, Issue , 2023.
Digital Object Identifier: 10.1109/ACCESS.2023.3267672 [CrossRef]
[2] Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks, Ribeiro, Tiago F.R., Silva, Fernando, Moreira, José, Costa, Rogério Luís de C., ISPRS Journal of Photogrammetry and Remote Sensing, ISSN 0924-2716, Issue , 2023.
Digital Object Identifier: 10.1016/j.isprsjprs.2023.07.002 [CrossRef]
[3] A Video Based Fire Smoke Detection Using Robust AdaBoost, Wu, Xuehui, Lu, Xiaobo, Leung, Henry, Sensors, ISSN 1424-8220, Issue 11, Volume 18, 2018.
Digital Object Identifier: 10.3390/s18113780 [CrossRef]
[4] Online Recognition Method for Target Maneuver in UAV Autonomous Air Combat, Li, Yicong, Yang, Zhen, Lv, Xiaofeng, Huang, Jichuan, Zhao, Yiyang, Zhou, Deyun, 2022 22nd International Conference on Control, Automation and Systems (ICCAS), ISBN 978-89-93215-24-3, 2022.
Digital Object Identifier: 10.23919/ICCAS55662.2022.10003924 [CrossRef]
[5] Application of Cogent Confabulation Classifier to bathing water quality assessment using remote sensing data, Ivanda, Antonia, Seric, Ljiljana, Braovic, Maja, Stipanicev, Darko, 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), ISBN 978-953-233-103-5, 2022.
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[6] A Large Scale Dataset For Fire Detection and Segmentation in Indoor Spaces, Maric, Petar, Arlovic, Matej, Balen, Josip, Vdovjak, Kresimir, Damjanovic, Davor, 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), ISBN 978-1-6654-7095-7, 2022.
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Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
