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Image Segmentation Using Clustering Methods

  • Benrais LamineEmail author
  • Baha Nadia
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Image segmentation is one of the most valuable processes in the field of artificial vision. It aims to extract objects from the scene by finding patterns between adjacent pixels. In this paper, we introduce a novel approach of object extraction by combining two well-known clustering methods: The K-means and DBSCAN. The aim is to explore the advantages of each one to minimize the mutual inconvenient. The mainidea is the use of two sources in the clusters construction: center homogeneity guided by the K-means algorithm and border homogeneity guided by the DBSCAN algorithm. Results are run on the BSDS500 dataset showing encouraging results comparing to the ground truth segmentation provided.

Keywords

Image segmentation K-means DBSCAN Clustering 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Science and Technology Houari BoumedieneAlgiersAlgeria

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