Superpixels for image and video processing based on proximity-weighted patch matching

Se Ho Lee, Won Dong Jang, Chang Su Kim

Research output: Contribution to journalArticle


In this paper, a temporal superpixel algorithm using proximity-weighted patch matching (PPM) is proposed to yield temporally consistent superpixels for image and video processing. PPM estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the superpixel itself. In each frame, we initialize superpixels by transferring the superpixel labels of the previous frame using PPM motion vectors. Then, we update the superpixel labels of boundary pixels by minimizing a cost function, which is composed of feature distance, compactness, contour, and temporal consistency terms. Finally, we carry out superpixel splitting, merging, and relabeling to regularize superpixel sizes and correct inaccurate labels. Extensive experimental results confirm that the proposed algorithm outperforms the state-of-the-art conventional algorithms significantly. Also, it is demonstrated that the proposed algorithm can be applied to video object segmentation and video saliency detection tasks.

Original languageEnglish
JournalMultimedia Tools and Applications
Publication statusAccepted/In press - 2020 Jan 1


  • Image processing
  • Image segmentation
  • Saliency detection
  • Superpixel
  • Temporal superpixel
  • Video processing

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Superpixels for image and video processing based on proximity-weighted patch matching'. Together they form a unique fingerprint.

  • Cite this