Computational Visual Media

, Volume 4, Issue 4, pp 333–348 | Cite as

FLIC: Fast linear iterative clustering with active search

  • Jiaxing Zhao
  • Ren BoEmail author
  • Qibin Hou
  • Ming-Ming Cheng
  • Paul Rosin
Open Access
Research Article


In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called “active search” which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering (SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence, and also provides better boundaries in the over-segmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest, achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available.


image over-segmentation SLIC neighbor continuity back-and-forth traversal 



This research was sponsored by National Natural Science Foundation of China (Nos. 61620106008 and 61572264), Huawei Innovation Research Program (HIRP), and IBM Global SUR Award.


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Authors and Affiliations

  • Jiaxing Zhao
    • 1
  • Ren Bo
    • 1
    Email author
  • Qibin Hou
    • 1
  • Ming-Ming Cheng
    • 1
  • Paul Rosin
    • 2
  1. 1.Nankai UniversityTianjinChina
  2. 2.Cardiff UniversityWalesUK

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