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Comparative Analysis of Different Clustering Techniques for Video Segmentation

  • Tunirani NayakEmail author
  • Nilamani Bhoi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

Video segmentation is an extremely challenging and active area in the field of video processing and computer vision. Video segmentation techniques can be classified basically into two approaches: one approach for which there are preassigned thresholds and another clustering approach for which the number of clusters has been used, which is known. Here, we have studied and analyzed the cluster-based techniques such as mean-shift, K-means, and fuzzy C-means segmentation algorithms. We have evaluated and compared the performances of segmentation methods qualitatively and also quantitatively. To calculate the different quantitative metrics, the images and ground truth of the CDnet 2014 database have been used.

Keywords

Video segmentation Mean-shift K-means Fuzzy C-means 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringVSSUTBurla, SambalpurIndia

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