Image Segmentation by Searching for Image Feature Density Peaks
Image segmentation attempts to classify the pixels of a digital image into multiple groups to facilitate subsequent image processing. It is an essential problem in many research areas such as computer vision and image processing application. A large number of techniques have been proposed for image...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-06-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/6/969 |
| _version_ | 1818425071994142720 |
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| author | Zhe Sun Meng Qi Jian Lian Weikuan Jia Wei Zou Yunlong He Hong Liu Yuanjie Zheng |
| author_facet | Zhe Sun Meng Qi Jian Lian Weikuan Jia Wei Zou Yunlong He Hong Liu Yuanjie Zheng |
| author_sort | Zhe Sun |
| collection | DOAJ |
| description | Image segmentation attempts to classify the pixels of a digital image into multiple groups to facilitate subsequent image processing. It is an essential problem in many research areas such as computer vision and image processing application. A large number of techniques have been proposed for image segmentation. Among these techniques, the clustering-based segmentation algorithms occupy an extremely important position in this field. However, existing popular clustering schemes often depends on prior knowledge and threshold used in the clustering process, or lack of an automatic mechanism to find clustering centers. In this paper, we propose a novel image segmentation method by searching for image feature density peaks. We apply the clustering method to each superpixel in an input image and construct the final segmentation map according to the classification results of each pixel. Our method can give the number of clusters directly without prior knowledge, and the cluster centers can be recognized automatically without interference from noise. Experimental results validate the improved robustness and effectiveness of the proposed method. |
| first_indexed | 2024-12-14T14:08:07Z |
| format | Article |
| id | doaj.art-7a05df1ea40c48c381281cc7ba5d972d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-14T14:08:07Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7a05df1ea40c48c381281cc7ba5d972d2022-12-21T22:58:25ZengMDPI AGApplied Sciences2076-34172018-06-018696910.3390/app8060969app8060969Image Segmentation by Searching for Image Feature Density PeaksZhe Sun0Meng Qi1Jian Lian2Weikuan Jia3Wei Zou4Yunlong He5Hong Liu6Yuanjie Zheng7School of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaDepartment of Electrical Engineering Information Technology, Shandong University of Science and Technology, Jinan 250031, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaYantai Lanyoung Electronic Co., Ltd. Hangtian Road No. 101th, Block B 402# , Yantai 264003, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaSchool of Information Science and Engineering, Shandong Normal University, Jinan 25030, ChinaImage segmentation attempts to classify the pixels of a digital image into multiple groups to facilitate subsequent image processing. It is an essential problem in many research areas such as computer vision and image processing application. A large number of techniques have been proposed for image segmentation. Among these techniques, the clustering-based segmentation algorithms occupy an extremely important position in this field. However, existing popular clustering schemes often depends on prior knowledge and threshold used in the clustering process, or lack of an automatic mechanism to find clustering centers. In this paper, we propose a novel image segmentation method by searching for image feature density peaks. We apply the clustering method to each superpixel in an input image and construct the final segmentation map according to the classification results of each pixel. Our method can give the number of clusters directly without prior knowledge, and the cluster centers can be recognized automatically without interference from noise. Experimental results validate the improved robustness and effectiveness of the proposed method.http://www.mdpi.com/2076-3417/8/6/969image segmentationclusteringdensity peaksrobust search |
| spellingShingle | Zhe Sun Meng Qi Jian Lian Weikuan Jia Wei Zou Yunlong He Hong Liu Yuanjie Zheng Image Segmentation by Searching for Image Feature Density Peaks Applied Sciences image segmentation clustering density peaks robust search |
| title | Image Segmentation by Searching for Image Feature Density Peaks |
| title_full | Image Segmentation by Searching for Image Feature Density Peaks |
| title_fullStr | Image Segmentation by Searching for Image Feature Density Peaks |
| title_full_unstemmed | Image Segmentation by Searching for Image Feature Density Peaks |
| title_short | Image Segmentation by Searching for Image Feature Density Peaks |
| title_sort | image segmentation by searching for image feature density peaks |
| topic | image segmentation clustering density peaks robust search |
| url | http://www.mdpi.com/2076-3417/8/6/969 |
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