Color Image Segmentation Using Fuzzy C-Regression Model
Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning technique...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2017/4582948 |
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| author | Min Chen Simone A. Ludwig |
| author_facet | Min Chen Simone A. Ludwig |
| author_sort | Min Chen |
| collection | DOAJ |
| description | Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms. |
| format | Article |
| id | doaj-art-d1d52257a325451b8756aca85d9d5af2 |
| institution | Kabale University |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-d1d52257a325451b8756aca85d9d5af22025-02-03T05:59:52ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2017-01-01201710.1155/2017/45829484582948Color Image Segmentation Using Fuzzy C-Regression ModelMin Chen0Simone A. Ludwig1State University of New York at New Paltz, New Paltz, NY, USANorth Dakota State University, Fargo, ND, USAImage segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.http://dx.doi.org/10.1155/2017/4582948 |
| spellingShingle | Min Chen Simone A. Ludwig Color Image Segmentation Using Fuzzy C-Regression Model Advances in Fuzzy Systems |
| title | Color Image Segmentation Using Fuzzy C-Regression Model |
| title_full | Color Image Segmentation Using Fuzzy C-Regression Model |
| title_fullStr | Color Image Segmentation Using Fuzzy C-Regression Model |
| title_full_unstemmed | Color Image Segmentation Using Fuzzy C-Regression Model |
| title_short | Color Image Segmentation Using Fuzzy C-Regression Model |
| title_sort | color image segmentation using fuzzy c regression model |
| url | http://dx.doi.org/10.1155/2017/4582948 |
| work_keys_str_mv | AT minchen colorimagesegmentationusingfuzzycregressionmodel AT simonealudwig colorimagesegmentationusingfuzzycregressionmodel |