TY - GEN
T1 - Cartoon image segmentation based on improved SLIC superpixels and adaptive region propagation merging
AU - Wu, Huisi
AU - Wu, Yilin
AU - Zhang, Shenglong
AU - Li, Ping
AU - Wen, Zhenkun
PY - 2016/8
Y1 - 2016/8
N2 - This paper present a novel algorithm for cartoon image segmentation based on the simple linear iterative clustering (SLIC) superpixels and adaptive region propagation merging. To break the limitation of the original SLIC algorithm in confirming to image boundaries, this paper proposed to improve the quality of the superpixels generation based on the connectivity constraint. To achieve efficient segmentation from the superpixels, this paper employed an adaptive region propagation merging algorithm to obtain independent segmented object. Compared with the pixel-based segmentation algorithms and other superpixel-based segmentation methods, the method proposed in this paper is more effective and more efficient by determining the propagation center adaptively. Experiments on abundant cartoon images showed that our algorithm outperforms classical segmentation algorithms with the boundary-based and region-based criteria. Furthermore, the final cartoon image segmentation results are also well consistent with the human visual perception.
AB - This paper present a novel algorithm for cartoon image segmentation based on the simple linear iterative clustering (SLIC) superpixels and adaptive region propagation merging. To break the limitation of the original SLIC algorithm in confirming to image boundaries, this paper proposed to improve the quality of the superpixels generation based on the connectivity constraint. To achieve efficient segmentation from the superpixels, this paper employed an adaptive region propagation merging algorithm to obtain independent segmented object. Compared with the pixel-based segmentation algorithms and other superpixel-based segmentation methods, the method proposed in this paper is more effective and more efficient by determining the propagation center adaptively. Experiments on abundant cartoon images showed that our algorithm outperforms classical segmentation algorithms with the boundary-based and region-based criteria. Furthermore, the final cartoon image segmentation results are also well consistent with the human visual perception.
KW - Adaptive region propagation
KW - Cartoon image segmentation
KW - SLIC superpixels
UR - http://www.scopus.com/inward/record.url?scp=85018665690&partnerID=8YFLogxK
U2 - 10.1109/SIPROCESS.2016.7888267
DO - 10.1109/SIPROCESS.2016.7888267
M3 - Conference article published in proceeding or book
AN - SCOPUS:85018665690
T3 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
SP - 277
EP - 281
BT - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Signal and Image Processing, ICSIP 2016
Y2 - 13 August 2016 through 15 August 2016
ER -