An Efficient Image Segmentation Algorithm for Object Recognition Using Spectral Clustering

  • Xiaochun WangEmail author
  • Xiali Wang
  • Don Mitchell Wilkes


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.


Image segmentation Object recognition Spectral clustering BIRCH tree 


  1. Bleyer, M., & Gelautz, M. (2005). Graph-based surface reconstruction from stereo pairs using image segmentation. In Proceedings of SPIE—The International Society for Optical Engineering (Vol. 5665, pp. 288–299). SPIE.Google Scholar
  2. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 679–698.CrossRefGoogle Scholar
  3. Ding, C. (2007). A tutorial on spectral clustering. Journal of Statistics and Computing, 17, 395–416.MathSciNetCrossRefGoogle Scholar
  4. Donath, W. E., & Hoffman, A. J. (1973). Lower bounds for the partitioning of graphs. IBM Journal of Research and Development, 17, 420–425.MathSciNetCrossRefGoogle Scholar
  5. Duarte, A., Sánchez, A., Fernández, F., & Montemayor, A. S. (2006). Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic. Pattern Recognition Letters, 27(11), 1239–1251.Google Scholar
  6. Favaro, P., & Soatto, S. (2004). A variational approach to scene reconstruction and image segmentation from motion blur cues. In Proceedings of IEEE International Conference of Computer Vision and Pattern Recognition (CVPR ‘04).Google Scholar
  7. Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M.-P., Moreau-Gobard, R., Ramaraj, R., et al. (2006). Automatic heart isolation for CT coronary visualization using graph-cut. In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI ‘06) (pp. 614–617).Google Scholar
  8. Gonzalez, R. C., & Woods, R. E. (1992). Digital image processing. Reading, MA: Addison Wesley.Google Scholar
  9. Grady, L., Sun, Y., & Williams, J. (2006). Three interactive graph-based segmentation methods applied to cardiovascular imaging. In Mathematical models in computer vision: The handbook (pp. 453–469). Springer.Google Scholar
  10. Jia, H., Ding, S., Xu, X., & Nie, R. (2014). The latest research progress on spectral clustering. Neural Computing and Applications, 24, 1477–1486.CrossRefGoogle Scholar
  11. Jolly, M. P., Xue, H., Grady, L., & Guehring, J. (2009). Combining registration and minimum surfaces for the segmentation of the left ventricle in cardiac cine MR images. In Proceedings of MICCAI (pp. 910–918).Google Scholar
  12. Lam, C. L., & Yuen, S. Y. (1998). An unbiased active contour algorithm for object tracking. Pattern Recognition Letters, 19(5–6), 491–498.MathSciNetCrossRefGoogle Scholar
  13. Meila, M., & Shi, J. (2001). A random walks view of spectral segmentation. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS ‘01).Google Scholar
  14. Ng, A., Jordan, M., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems (pp. 849–856).Google Scholar
  15. Ning, J., Zhang, L., Zhang, D., & Wu, C. (2010). Interactive image segmentation by maximal similarity based region merging. Pattern Recognition, 43, 445–456.CrossRefGoogle Scholar
  16. Paul, B., Zhang, L., & Wu, X. (2005). Canny edge detection enhancement by scale multiplication. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1485–1490.CrossRefGoogle Scholar
  17. Peng, B., Zhang, L., & Zhang, D. (2013). A survey of graph theoretical approaches to image segmentation. Pattern Recognition, 46(3), 1020–1038.Google Scholar
  18. Russell, B., Efros, A., Sivic, J., Freeman, W., & Zisserman, A. (2006). Using multiple segmentations to discover objects and their extent in image collections. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR ‘06).Google Scholar
  19. Seo, K. H., Shin, J. H., Kim, W., & Lee, J. J. (2006). Real-time object tracking and segmentation using adaptive color snake model. International Journal of Control, Automation and Systems, 4(2), 236–246.Google Scholar
  20. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888–905.CrossRefGoogle Scholar
  21. Tian, Z., Ranghu, R., & Miron, L. (1996). BIRCH: An efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD international conference on Management of data (SIGMOD ‘96) (pp. 103–114).Google Scholar
  22. Unnikrishnan, R., Pantofaru, C., & Hebert, M. (2007). Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 929–944.CrossRefGoogle Scholar
  23. Wang, X., Wang, X. L., Chen, C., & Wilkes, D. M. (2013). Enhancing minimum spanning tree based clustering by removing density based outliers. Digital Signal Processing, 23(5), 1523–1538.Google Scholar
  24. Wang, X., Wang, X. L., & Wilkes, D. M. (2009). A divide and conquer approach for minimum spanning tree based clustering. IEEE Transactions on Knowledge and Data Engineering, 21(7), 945–958.CrossRefGoogle Scholar
  25. Wertheimer, M. (1938). Laws of organization in perceptual forms (partial translation). In W. B. Ellis (Ed.), A sourcebook of gestalt psycychology (pp. 71–88). Brace: Harcourt.CrossRefGoogle Scholar
  26. Yan, D., Huang, L., & Michael, I. J. (2009). Fast approximate spectral clustering. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD ‘09) (pp. 907–916).Google Scholar
  27. Yu, S., Gross, R., & Shi, J. (2002, December). Concurrent object segmentation and recognition with graph partitioning. In Proceedings of Neural Information Processing Systems (NIPS) (pp. 1383–1390).Google Scholar

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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