1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Region-based image retrieval using integrated color, shape, and location index Presenter : You Lin Chen Authors :B.G. Prasad,*,1K.K. Biswas, and S.K. Gupta* 2004.CVIU.

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Methodology Experiments Conclusion Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation In general, the image features tend to capture only some of the aspects of image similarity. It is unclearly specify what or how a user should initiate queries. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives We propose a system that indexes and retrieves similar images based on a composite color–shape–location index. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Lorentzian error norm Methodology Anisotropic di ff usion 5 Huber’s minmax norm Tukey’s biweight norm

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 6 Color matching and region selection

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Scale normalization Rotation normalization 180 。 rotation horizontal rotation vertical rotation Methodology Shape basic scheme 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Color indexing 10 Shape indexing Location indexing Composite indexing

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion Although we have implemented the integrated color–shape– location indexing on domains of flags, flowers.etc Indexing scheme can be applied to other domain. 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Comments Advantage  This idea is easy to understand. Drawback  … Application  Image Processing