|2/2017 - 12|
Cogent Confabulation based Expert System for Segmentation and Classification of Natural Landscape ImagesBRAOVIC, M. , STIPANICEV, D. , KRSTINIC, D.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (6,154 KB) | Citation | Downloads: 276 | Views: 1,325|
expert systems, image classification, image color analysis, image segmentation, knowledge engineering
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
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.
|References|||||Cited By «-- Click to see who has cited this paper|
| M. Bugaric, T. Jakovcevic, D. Stipanicev, "Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index", Computer Vision and Image Understanding, vol. 118, pp. 184-196, 2014. |
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]
 D. Krstinic, T. Jakovcevic, D. Stipanicev, "Histogram-based smoke segmentation in forest fire detection system", Information Technology and Control, vol. 38, no. 3, pp. 237-244, 2009.
 T. Jakovcevic, D. Stipanicev, D. Krstinic, "Visual spatial-context based wildfire smoke sensor", Machine Vision and Applications, vol. 24, issue 4, pp. 707-719, May 2013.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 15]
 T. Jakovcevic, "Wildfire-smoke detection based on visible-spectrum image analysis" (In Croatian: "Detekcija dima pozara raslinja analizom slika dobivenih u vidljivom dijelu spektra"), doctoral dissertation, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia, 2011.
 R. Hecht-Nielsen, "Cogent Confabulation", Neural Networks, vol. 18, no. 2, pp. 111-115, March 2005.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 37]
 R. Hecht-Nielsen, "The Mechanism of Thought", International Joint Conference on Neural Networks, Vancouver, Canada, pp. 419-426, July, 16-21 2006.
 E. A. Khan, E. Reinhard, "Evaluation of color spaces for edge classification in outdoor scenes", IEEE International Conference on Image Processing, vol. 3, pp. 952-5, 2005.
[CrossRef] [SCOPUS Times Cited 20]
 J. M. Chaves-Gonzalez, M. A. Vega-Rodriguez, J. A. Gomez-Pulido, J. M. Sanchez-Perez, "Detecting skin in face recognition systems: A colour spaces study", Digital Signal Processing, vol. 20, issue 3, pp. 806-823, 2010.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 135]
 Y.-C. Wang, C.-C. Han, C.-T. Hsieh, K.-C. Fan, "Vehicle color classification using manifold learning methods from urban surveillance videos", EURASIP Journal on Image and Video Processing, 2014.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 2]
 H. Stokman, T. Gevers, "Selection and fusion of color models for image feature detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 371-381, 2007.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 93]
 A. Bosch, X. Munoz, J. Freixenet, "Segmentation and description of natural outdoor scenes", Image and Vision Computing, vol. 25, issue 5, pp. 727-740, 2007.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 48]
 J. Marti, J. Freixenet, J. Batlle, A. Casals, "A new approach to outdoor scene description based on learning and top-down segmentation", Image and Vision Computing, vol. 19, issue 14, pp. 1041-1055, 2001.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 13]
 V. Burak Celen, M. Fatih Demirci, "Fire detection in different color models", Proceedings of the 2012 International Conference on Image Processing, Computer Vision, & Pattern Recognition, 2012.
 T. Çelik, H. Ozkaramanli, H. Demirel, "Fire and smoke detection without sensors: image processing based approach", 15th European Signal Processing Conference (EUSIPCO 2007), pp. 1794-1798, 2007.
 J. J. de Dios, N. Garcia, "Face detection based on a new color space YCgCr", International Conference on Image Processing, pp. III-909-III-912, 2003.
 Y.-I. Ohta, T. Kanade, T. Sakai, "Color information for region segmentation", Computer Graphics and Image Processing, vol. 13, pp. 222-241, 1980.
 D. Stipanicev, Lj. Seric, M. Braovic, D. Krstinic, T. Jakovcevic, M. tula, M. Bugaric, J. Maras, "Vision based wildfire and natural risk observers", 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 37-42, 15-18 October 2012.
[CrossRef] [SCOPUS Times Cited 6]
 M. Sokolova, N. Japkowicz, S. Szpakowicz, "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation", AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, vol. 4304, pp. 1015-1021, 2006.
 D. Stipanicev, "Intelligent forest fire monitoring system - from idea to realization", Annual 2010/2011 of the Croatian Academy of Engineering, pp. 58-73, 2012.
 T. Roncevic, M. Braovic, D. Stipanicev, "Non-parametric context-based object classification in images", Information Technology and Control. vol. 46, no. 1, pp. 86-99, 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]
 M. Braovic, "Segmentation and classification of non-transparent and semi-transparent regions on natural landscape images" (In Croatian: Segmentacija i klasifikacija neprozirnih i poluprozirnih regija na slikama prirodnog krajolika), doctoral dissertation, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia, 2015.
 M. Braovic, "Color based region classification in Mediterranean landscape images", Abstract Book - Fourth Croatian Computer Vision Workshop / Editors: Sven Loncaric and Josip Krapac, Zagreb, 2015.
 M. Braovic, "Color-based region classification in Mediterranean landscape images", The 2nd ACROSS Workshop on Advanced Cooperative Systems, Poster Session, Zagreb, Croatia, 2016.
Web of Science® Citations for all references: 275 TCR
SCOPUS® Citations for all references: 382 TCR
Web of Science® Average Citations per reference: 11 ACR
SCOPUS® Average Citations per reference: 16 ACR
TCR = Total Citations for References / ACR = Average Citations per Reference
We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more
Citations for references updated on 2020-02-22 16:43 in 99 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
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.