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Appl. Sci. 2018, 8(6), 969;

Image Segmentation by Searching for Image Feature Density Peaks

School of Information Science and Engineering, Shandong Normal University, Jinan 25030, China
Department of Electrical Engineering Information Technology, Shandong University of Science and Technology, Jinan 250031, China
Yantai Lanyoung Electronic Co., Ltd. Hangtian Road No. 101th, Block B 402# , Yantai 264003, China
Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan 250300, China
Key Lab of Intelligent Computing & Information Security in Universities of Shandong, Shandong Normal University, Jinan 250300, China
Institute of Biomedical Sciences, Key Lab of Intelligent Information Processing, Shandong Normal University, Jinan 250300, China
Authors to whom correspondence should be addressed.
Received: 14 May 2018 / Revised: 2 June 2018 / Accepted: 9 June 2018 / Published: 13 June 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
Full-Text   |   PDF [17703 KB, uploaded 13 June 2018]   |  


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. View Full-Text
Keywords: image segmentation; clustering; density peaks; robust search image segmentation; clustering; density peaks; robust search

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Sun, Z.; Qi, M.; Lian, J.; Jia, W.; Zou, W.; He, Y.; Liu, H.; Zheng, Y. Image Segmentation by Searching for Image Feature Density Peaks. Appl. Sci. 2018, 8, 969.

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