1 / 17

Complex Zernike Moments Features for Shape-Based Image Retrieval

Complex Zernike Moments Features for Shape-Based Image Retrieval. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 1, JANUARY 2009. 指導教授：李育強 報告者 ：楊智雁 日期 ： 2010/03/15. Outline. 1. Introduction. Zernike Moments. 2. Zm Phase and Magnitude. 3.

illiana-spencer
Download Presentation

Complex Zernike Moments Features for Shape-Based Image Retrieval

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Complex Zernike Moments Features forShape-Based Image Retrieval IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 39, NO. 1, JANUARY 2009 指導教授：李育強 報告者 ：楊智雁 日期 ：2010/03/15

  2. Outline 1 Introduction Zernike Moments 2 Zm Phase and Magnitude 3 Experiments 4 5 Conclusion

  3. 1. Introduction • Existing CBIR systems can be broadly categorized into two groups • Contour and region-based descriptors • As the most commonly used approaches for region-based shape descriptors (geometric moments)

  4. 1. Introduction (c.) • However, geometric moments do not have any of the desired invariance • Such as translation, scale, or rotation invariance • In this paper, we try to find out the relative importance of the phase and magnitude of Zernike Moments

  5. 2. Zernike Moments Radial polynomials Basis Function

  6. 2. Zernike Moments (c.) • Zernike moments measurement

  7. 3. Zm Phase and Magnitude • The reconstructed images have far less resemblance to the original image than those by using both magnitude and phase components

  8. 3. Zm Phase and Magnitude (c.) The corrected phase angle of the rotated image is the same as the corrected phase angle of the nonrotated image

  9. 3. Zm Phase and Magnitude (c.) • Angle-based distance and magnitude-based distance

  10. 4. Experiments • A. Preparation for Test DBs • Scale test DB 、Rotation test DB 、Subject test DB、 Noisy test DB • B. Measurement of Retrieval Performance • 1.P−R Graph • 2.BEP

  11. 4. Experiments (c.) • C.Experiment Results • 1.IZMD With Different Max Orders

  12. 4. Experiments (c.) • 2.Performance Comparison of IZMD and ZMD

  13. 4. Experiments (c.)

  14. 4. Experiments (c.) • 3.Performance Comparison of IZMD and GFD

  15. 4. Experiments (c.)

  16. 5.Conclusion • Rotation、 translation、 scaling or change in viewpoint，We propose here IZMD for robust image retrieval • Its superior performance in noise robustness and subject discriminability when compared with magnitude-only ZMD • We would incorporate object segmentation techniques into the proposed IZMD framework

  17. Thank You !

More Related