Techniques for CBIR 03/10/16陳慶鋒

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

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

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

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

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

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

Iteration-free clustering (cont.)

Algorithm Algorithm a. Generating separating hyperplane a. Generating separating hyperplane b. Updating separating hyperplanes using b. Updating separating hyperplanes using IFC algorithm IFC algorithm

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

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

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

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

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

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

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

Iteration-free clustering (cont.)

Simulation result

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

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