SSGD: Superpixels using the shortest gradient distance

Ning Zhang, Lin Zhang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)


As a pre-processing step for many problems in the field of computer vision, superpixel algorithms aim to over-segment the image by grouping homogenous pixels. In this paper, we propose a novel superpixel segmentation algorithm, namely Superpixel using the Shortest Gradient Distance (SSGD for short) in a κ-means clustering framework. Starting from initializing the superpixel seeds, bilateral filtering is applied to the texture-rich regions centered at initial seeds. Then, a novel distance function taking the shortest gradient distance into account is computed to enforce adherence to boundaries. Unlike using the simple Euclidean distance, the proposed combined distance function increases the accuracy of associating a pixel to a cluster. The experimental results demonstrate that our algorithm outperforms the state-of-the-art methods in this field. Source codes of SSGD are publicly available at
Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing (ICIP)
Number of pages5
Publication statusPublished - 22 Feb 2018
Externally publishedYes
Event2017 IEEE International Conference on Image Processing (ICIP) -
Duration: 17 Sept 201720 Sept 2017


Conference2017 IEEE International Conference on Image Processing (ICIP)


  • ITC-CV


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