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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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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)
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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 13] [SCOPUS Times Cited 15]
 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 18] [SCOPUS Times Cited 18]
 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 39] [SCOPUS Times Cited 42]
 R. Hecht-Nielsen, "The Mechanism of Thought", International Joint Conference on Neural Networks, Vancouver, Canada, pp. 419-426, July, 16-21 2006.
[CrossRef] [SCOPUS Times Cited 19]
 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 22]
 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 112] [SCOPUS Times Cited 166]
 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 79] [SCOPUS Times Cited 100]
 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 33] [SCOPUS Times Cited 51]
 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 14]
 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 2] [SCOPUS Times Cited 3]
 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.
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