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Symmetry 2017, 9(3), 31; doi:10.3390/sym9030031

BSLIC: SLIC Superpixels Based on Boundary Term

1
Electro-Mechanical Engineering School, Xidian University, Xi’an 710071, China
2
State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ka Lok Man
Received: 24 November 2016 / Revised: 16 February 2017 / Accepted: 22 February 2017 / Published: 26 February 2017
(This article belongs to the Special Issue Symmetry in Systems Design and Analysis)
View Full-Text   |   Download PDF [5104 KB, uploaded 26 February 2017]   |  

Abstract

A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited region, the generated superpixels are shaped into regular and compact hexagons. The additional cluster centers are initialized as edge pixels to improve boundary adherence, which is further promoted by incorporating the boundary term into the distance calculation of the k-means clustering. Berkeley Segmentation Dataset BSDS500 is used to qualitatively and quantitatively evaluate the proposed BSLIC method. Experimental results show that BSLIC achieves an excellent compromise between boundary adherence and regularity of size and shape. In comparison with SLIC, the boundary adherence of BSLIC is increased by at most 12.43% for boundary recall and 3.51% for under segmentation error. View Full-Text
Keywords: superpixel; SLIC; edge pixel; boundary term superpixel; SLIC; edge pixel; boundary term
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, H.; Peng, X.; Xiao, X.; Liu, Y. BSLIC: SLIC Superpixels Based on Boundary Term. Symmetry 2017, 9, 31.

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