Jump to : Download | Abstract | Contact | BibTex reference | EndNote reference |


Zhenguo Li, Xiao-Ming Wu, Shih-Fu Chang. Segmentation Using Superpixels: A Bipartite Graph Partitioning Approach. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

Download [help]

Download paper: Adobe portable document (pdf)

Copyright notice:This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


Grouping cues can affect the performance of segmentation greatly. In this paper, we show that superpixels (image segments) can provide powerful grouping cues to guide segmentation, where superpixels can be collected easily by (over)-segmenting the image using any reasonable existing segmentation algorithms. Generated by different algorithms with varying parameters, superpixels can capture diverse and multi-scale visual patterns of a natural image. Successful integration of the cues from a large multitude of superpixels presents a promising yet not fully explored direction. In this paper, we propose a novel segmentation framework based on bipartite graph partitioning, which is able to aggregate multi-layer superpixels in a principled and very effective manner. Computationally, it is tailored to unbalanced bipartite graph structure and leads to a highly efficient, linear-time spectral algorithm. Our method achieves significantly better performance on the Berkeley Segmentation Database compared to state-of-the-art techniques


Zhenguo Li
Xiao-Ming Wu
Shih-Fu Chang

BibTex Reference

   Author = {Li, Zhenguo and Wu, Xiao-Ming and Chang, Shih-Fu},
   Title = {Segmentation Using Superpixels: A Bipartite Graph Partitioning Approach},
   BookTitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
   Year = {2012}

EndNote Reference [help]

Get EndNote Reference (.ref)


For problems or questions regarding this web site contact The Web Master.

This document was translated automatically from BibTEX by bib2html (Copyright 2003 © Eric Marchand, INRIA, Vista Project).