Modified CLPSO-based fuzzy classification System: Color Image Segmentation
Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by select...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2014-07-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_356_df6c8577e418660ce36a70517abb7405.pdf |
| _version_ | 1819064896103383040 |
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| author | A.M. Shafiee A. M. Latif |
| author_facet | A.M. Shafiee A. M. Latif |
| author_sort | A.M. Shafiee |
| collection | DOAJ |
| description | Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification system with minimum number of fuzzy rules and minimum number of incorrectly classified patterns. In the CLPSO-based method, each individual of the population is considered to automatically generate a fuzzy classification system. Afterwards, a population member tries to maximize a fitness criterion which is high classification rate and small number of fuzzy rules. To reduce the multidimensional search space for an M-class classification problem, centroid of each class is calculated and then fixed in membership function of fuzzy system. The performance of the proposed method is evaluated in terms of future classification within the RoboCup soccer environment with spatially varying illumination intensities on the scene. The results present 85.8% accuracy in terms of classification. |
| first_indexed | 2024-12-21T15:37:51Z |
| format | Article |
| id | doaj.art-cb5c2a03029c4aa38645f11d92c8d63b |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-21T15:37:51Z |
| publishDate | 2014-07-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-cb5c2a03029c4aa38645f11d92c8d63b2022-12-21T18:58:36ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442014-07-012216717910.22044/jadm.2014.356356Modified CLPSO-based fuzzy classification System: Color Image SegmentationA.M. Shafiee0A. M. Latif1Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, IranDepartment of Electrical and Computer Engineering, Yazd University, Yazd, IranFuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification system with minimum number of fuzzy rules and minimum number of incorrectly classified patterns. In the CLPSO-based method, each individual of the population is considered to automatically generate a fuzzy classification system. Afterwards, a population member tries to maximize a fitness criterion which is high classification rate and small number of fuzzy rules. To reduce the multidimensional search space for an M-class classification problem, centroid of each class is calculated and then fixed in membership function of fuzzy system. The performance of the proposed method is evaluated in terms of future classification within the RoboCup soccer environment with spatially varying illumination intensities on the scene. The results present 85.8% accuracy in terms of classification.http://jad.shahroodut.ac.ir/article_356_df6c8577e418660ce36a70517abb7405.pdfComprehensive learning particle swarm optimizationFuzzy classificationImage segmentationPattern Recognition |
| spellingShingle | A.M. Shafiee A. M. Latif Modified CLPSO-based fuzzy classification System: Color Image Segmentation Journal of Artificial Intelligence and Data Mining Comprehensive learning particle swarm optimization Fuzzy classification Image segmentation Pattern Recognition |
| title | Modified CLPSO-based fuzzy classification System: Color Image Segmentation |
| title_full | Modified CLPSO-based fuzzy classification System: Color Image Segmentation |
| title_fullStr | Modified CLPSO-based fuzzy classification System: Color Image Segmentation |
| title_full_unstemmed | Modified CLPSO-based fuzzy classification System: Color Image Segmentation |
| title_short | Modified CLPSO-based fuzzy classification System: Color Image Segmentation |
| title_sort | modified clpso based fuzzy classification system color image segmentation |
| topic | Comprehensive learning particle swarm optimization Fuzzy classification Image segmentation Pattern Recognition |
| url | http://jad.shahroodut.ac.ir/article_356_df6c8577e418660ce36a70517abb7405.pdf |
| work_keys_str_mv | AT amshafiee modifiedclpsobasedfuzzyclassificationsystemcolorimagesegmentation AT amlatif modifiedclpsobasedfuzzyclassificationsystemcolorimagesegmentation |