원문정보
초록
영어
An improved SLIC method using uniform segmentation and reciprocal nearest neighbor (RNN) clustering is presented in this paper. This approach is made of two steps. First, image is segmented to a large number of regular homogeneous and small regions which are similar to cell. Second, instead of the original image pixels, small regions segmented are regarded as input of RNN clustering. A new similarity criterion is decided by regional diversity of average value normalized and variance. Regional constraint filter limited the large size of superpixel guarantees the uniformity and compactness of superpixel. Finally, the regions in a small range of distance are merged by RNN clustering. Results of experiment on BSDS 500 dataset of natural images show the proposed method has advantages of high boundary recall and low under-segmentation error over SLIC on small numbers superpixel.
목차
1. Introduction
2. The Related Techniques
2.1. SLIC
2.2 Reciprocal nearest Neighbor Clustering
3. The improved SLIC Approach using RNN clustering
3.1. Uniform Segmentation
3.2. RNN Region Merging
4. Experiments and Analysis
5. Conclusion
ACKNOWLEDGEMENTS
References