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Techniques for CBIR

Techniques for CBIR. 03/10/16 陳慶鋒. Outline. Iteration-free clustering algorithm for nonstationary image database Simulation result Possible research domain References. Iteration-free clustering. Nonstationary image database feature-based indexing method

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Techniques for CBIR

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  1. Techniques for CBIR 03/10/16 陳慶鋒

  2. Outline • Iteration-free clustering algorithm for nonstationary image database • Simulation result • Possible research domain • References

  3. Iteration-free clustering • Nonstationary image database feature-based indexing method ex:histogram,ccv… indexing structures ex:binary tree, R-tree…. images may be added or deleted from the database

  4. Iteration-free clustering (cont.) • K-mean clustering optimal clustering, but time consuming • Iteration-free clustering sub-optimal clustering, but more efficient

  5. Iteration-free clustering (cont.) • Algorithm a. Generating separating hyperplane b. Updating separating hyperplanes using IFC algorithm

  6. Iteration-free clustering (cont.) • Generating separating hyperplane: initial hyperplane: generated by k-mean algorithm

  7. Iteration-free clustering (cont.) • 2-D feature space

  8. Iteration-free clustering (cont.)

  9. Iteration-free clustering (cont.)

  10. Iteration-free clustering (cont.)

  11. Iteration-free clustering (cont.) • Algorithm a. Generating separating hyperplane b. Updating separating hyperplanes using IFC algorithm

  12. Iteration-free clustering (cont.) • Updating separating hyperplanes using IFC algorithm 1) Translation of hyperplanes 2) Rotation of hyperplanes

  13. Iteration-free clustering (cont.) • Translation of hyperplanes first partitions the new-coming feature vectors according to original hyperplane

  14. Iteration-free clustering (cont.) • Translation of hyperplanes(cont.) The database’s midvector becomes m’ instead of m.

  15. Iteration-free clustering (cont.) • The suboptimal midvector m’ outperforms the midvector of KMIO

  16. Iteration-free clustering (cont.) • Rotation of hyperplanes To obtain the rotation of the new hyperplane H’, the best representative line segment must be found first. Distance of x and :

  17. Iteration-free clustering (cont.) • Rotation of hyperplanes(cont.) is estimated according to the four vectors ,rather than by reapplying K-mean algorithm to determine new representative feature vectors. the cost function F:

  18. Iteration-free clustering (cont.) • Rotation of hyperplanes(cont.) The best representative line segment must have minimum cost and pass through the new midvector m’. Thus, the Lagragian function L is:

  19. Iteration-free clustering (cont.)

  20. Simulation result

  21. Possible Research Domain • New feature vectors for CBIR • New indexing structure for image database

  22. References [2]Chia H. Yeh, Chung J. Kuo, “Iteration-free clustering algorithm for nonstationary image database,” Multimedia, IEEE Transaction on, vol. 5, no. 2, JUNE 2003, pp. 223-236

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