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An Efficient Image Segmentation Algorithm for Object Recognition Using Spectral Clustering

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes
Chapter

Abstract

Being a crucial and challenging problem in computer vision, image segmentation refers to partitioning an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image and is the very first step for recognizing objects in the images. A large number of available algorithms for image segmentation are prone to either time inefficiency or low-accuracy in finding objects among multiple images. In this chapter, we employ spectral clustering to address this problem. Being one of the most popular modern clustering algorithms, spectral clustering is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the K-means algorithm. However, it is not very well scalable to large datasets. To partially circumvent this problem, in this chapter, we propose an integration-based fast incremental spectral clustering algorithm which is particularly designed for object recognition oriented image segmentation tasks for robotic applications in an outdoor unknown environment. The algorithm applies spectral clustering to each image, and then integrates the clustering results using a BIRCH tree. Experiments performed on image data demonstrate the efficacy of the proposed method.

Keywords

Image segmentation Object recognition Spectral clustering BIRCH tree 

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Copyright information

© Xi'an Jiaotong University Press 2020

Authors and Affiliations

  • Xiaochun Wang
    • 1
    Email author
  • Xiali Wang
    • 2
  • Don Mitchell Wilkes
    • 3
  1. 1.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Information EngineeringChang’an UniversityXi’anChina
  3. 3.Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleUSA

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