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. Copyright © 2016 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
|Title of host publication||Proceedings of 2016 IEEE International Conference on Signal and Image Processing (ICSIP)|
|Place of Publication||Danvers, MA|
|ISBN (Print)||9781509023776, 9781509023769|
|Publication status||Published - 2016|
CitationWu, H., Wu, Y., Zhang, S., Li, P., & Wen, Z. (2016). Cartoon image segmentation based on improved SLIC superpixels and adaptive region propagation merging. In Proceedings of 2016 IEEE International Conference on Signal and Image Processing (ICSIP) (pp. 277-281). Danvers, MA: IEEE.
- Image segmentation
- Algorithm design and analysis
- Image color analysis
- Clustering algorithms