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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2019> 28(3): 418-427
Jianzhong Wang, Pengzhan Liu, Jiadong Shi, Guodong Yan. Improved SLIC Segmentation Algorithm for Artificial Structure Images[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 418-427. doi: 10.15918/j.jbit1004-0579.18049
Citation: Jianzhong Wang, Pengzhan Liu, Jiadong Shi, Guodong Yan. Improved SLIC Segmentation Algorithm for Artificial Structure Images[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 418-427.doi:10.15918/j.jbit1004-0579.18049
shu

Improved SLIC Segmentation Algorithm for Artificial Structure Images

doi:10.15918/j.jbit1004-0579.18049

  • State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2018-09-11
    Abstract
  • Simple linear iterative cluster(SLIC) is widely used because controllable superpixel number, accurate edge covering, symmetrical production and fast speed of calculation. The main problem of the SLIC algorithm is its under-segmentation when applied to segment artificial structure images with unobvious boundaries and narrow regions. Therefore, an improved clustering segmentation algorithm to correct the segmentation results of SLIC is presented in this paper. The allocation of pixels is not only related to its own characteristic, but also to those of its surrounding pixels.Hence, it is appropriate to improve the standard SLIC through the pixels by focusing on boundaries. An improved SLIC method adheres better to the boundaries in the image is proposed, by using the first and second order difference operators as magnified factors. Experimental results demonstrate that the proposed method achieves an excellent boundary adherence for artificial structure images. The application of the proposed method is extended to images with an unobvious boundary in the Berkeley Segmentation Dataset BSDS500. In comparison with SLIC, the boundary adherence is increased obviously.
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