FLIC: Fast Linear Iterative Clustering With Active Search

In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm named “active search” which explicitly considers neighboring 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 works, such as Simple Linear Iterative Clustering (SLIC) and its follow-ups, who use fixed search regions and perform the assignment and the update step separately, our novel scheme reduces the number of iterations required for convergence, and also improves the boundary sensitivity of the over-segmentation results. Extensive evaluations on the Berkeley segmentation benchmark verify that our method outperforms competing methods under various evaluation metrics. In particular, lowest time cost is reported among existing methods (approximately 30 fps for a 481 × 321 image on a single CPU core). To facilitate the development of over-segmentation, the code will be publicly available.

Authors

• Jiaxing Zhao
• Bo Ren Tencent Youtu Lab, China
• Qibin Hou VCIP, School of Computer Science, Nankai University
• Ming-Ming Cheng Nankai University NKIARI
• Paul Rosin

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