1. Invariant Indexing Resistant to RST and Lossy Compression for DWT SPIHT Coded Images 資工研二陳慶鋒

2. Outline Motivation Motivation Algorithm Algorithm Simulation results Simulation results Future work Future work Reference Reference

3. Motivation A image may be changed by: A image may be changed by: RST RST lossy Compression lossy Compression histogram processing histogram processing We want find a invariant feature resistant to the above attacks from the image’s content We want find a invariant feature resistant to the above attacks from the image’s content

4. Algorithm The flow chart The flow chart ic SPIHT Sign Map DWTfeature i’c’ SPIHT Sign Map’ DWTfeature’ SimilarityMatching

5. Algorithm(cont.) Get Significance Map(s) Get Significance Map(s) ic SPIHT Sign Map DWTfeature i’c’ SPIHT Sign Map’ DWTfeature’ SimilarityMatching

6. Get Significance Maps We get the LSP of LL,LH,HL and HH band of the last level for each threshold from SPIHT bit stream,and then convert them to Significance Maps We get the LSP of LL,LH,HL and HH band of the last level for each threshold from SPIHT bit stream,and then convert them to Significance Maps

7. Get Significance Maps(cont.) The first threshold:32 The following threshold:16 63403320 48301810 15402724 1222394 6340332048301810 15402724 1222394 11101000 0100 0010 00010100 0011 0100 n=num of thresholds So we get 4*n Sign maps

8. Algorithm(cont.) Get Feature: autocorrelograms of 1s Get Feature: autocorrelograms of 1s ic SPIHT Sign Map DWTfeature i’c’ SPIHT Sign Map’ DWTfeature’ SimilarityMatching

9. Get Feature: autocorrelograms of 1s Choose some significance map(s) of some threshold(s), get autocorrelograms of 1s of it(them) using L1(L2) diatance Choose some significance map(s) of some threshold(s), get autocorrelograms of 1s of it(them) using L1(L2) diatance

10. Example of autocorrelograms of 1s using L1 distance 1 sign map 1 sign map 2 sign maps 2 sign maps 11 10 11101010 12112 110211212

11. Algorithm(cont.) The similarity measure The similarity measure using I ntersection using I ntersection ic SPIHT Sign Map DWTfeature i’c’ SPIHT Sign Map’ DWTfeature’ SimilarityMatching

12. Simulation results Setup Setup image: LENA compared image: LENA with rotation, scale,and translation JPEG compression JPEG compression FMLR FMLRBABOON PEPPER PEPPER

13. Simulation results(cont.) Setup(cont.) Setup(cont.) image size: 512*512 DWT filter: f 9/7 DWT level: 5 the last subband size: 16*16 numbers of threshold: 5

14. Simulation results(cont.) See the EXCEL

15. Future work Compare with other methods Compare with other methods wavelet autocorrelogram EZW histogram Add autocorrelogram of LIS Add autocorrelogram of LIS Using color images (YCbCr or RGB) Using color images (YCbCr or RGB)

16. Reference Amir Said and William A. Pearlman,”A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchial Trees”,IEEE trans. On Circuits and Systems for Video Technology, vol. 6, no.3, pp. 243- 250, June 1996. Amir Said and William A. Pearlman,”A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchial Trees”,IEEE trans. On Circuits and Systems for Video Technology, vol. 6, no.3, pp. 243- 250, June 1996. [4] J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R.Zabih, “Image indexing using color correlograms,” Conf. Computer Vision and Pattern Recognit., pp.762-768,1997 [4] J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R.Zabih, “Image indexing using color correlograms,” Conf. Computer Vision and Pattern Recognit., pp.762-768,1997