Earticle

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

Abstract
 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

키워드

저자

• Junrui Lv [ School of mathematics and computer science, Panzhihua University, Si chuan Panzhihua 617000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605