1 / 20

Image Hashing for DWT SPIHT Coded Images

Image Hashing for DWT SPIHT Coded Images. 陳慶鋒. Outline. Image hashing The significance maps from SPIHT The SPIHT-autocorrelogram Distance(similarity) measure Experimental results Future work. Image hashing. Watermarking Content-based image retrieval(CBIR) Image hashing.

harsha
Télécharger la présentation

Image Hashing for DWT SPIHT Coded Images

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. Image Hashing for DWT SPIHT Coded Images 陳慶鋒

  2. Outline • Image hashing • The significance maps from SPIHT • The SPIHT-autocorrelogram • Distance(similarity) measure • Experimental results • Future work

  3. Image hashing • Watermarking • Content-based image retrieval(CBIR) • Image hashing

  4. The significance maps from SPIHT • SPIHT Initialization Sorting pass Refinement pass Quantization-step update output: bit stream

  5. The significance maps from SPIHT • In sorting pass, we can get the significance of each entry in LIP and LIS(A type and B type). So we form the significance maps according to the above property. • Only the last 4 subbands are considered

  6. The significance maps from SPIHT • examples

  7. The significance maps from SPIHT • example LIP LIS(A) LIS(B)

  8. The SPIHT-autocorrelogram • Histogram-based method in CBIR ex: CCV,color correlogram,etc property: contain both color and spatial information resistant to geometric distortion

  9. The SPIHT-autocorrelogram • Count the autocorrelogram of 1’s for each significance map let a significance map M be a mxm matrix , means its value

  10. The SPIHT-autocorrelogram • Count the autocorrelogram of 1’s for each significance map let a distance the autocorrelogram of 1’s of M is defined as

  11. The SPIHT-autocorrelogram • example

  12. Distance(similarity) measure • For the significance maps or the SPIHT-autocorrelograms, convert them to an one-dimension vector as our hash.

  13. Distance(similarity) measure • Distance measure using L1 distance let H and H’ be the hashes of two iamges Hi means the value of the ith entry in H the L1 distance between two hashes is defined as

  14. Experimental Results • Setup database: 900images(100 different images and 800 attacked images) color space: YCbCr DWT: 9/7f level: 5 the thresholds: the first 3 thresholds sign maps per image: 3*3*4*3=108

  15. A1 Gaussian filtering 3x3 A2 Sharpening 3x3 A3 median filter 3x3 A4 FMLR A5 random bend A6 JPEG 20% A7 flip A8 ratation 90 degree Experimental Results • Attack modes

  16. Experimental Results • Example of attacked images

  17. Experimental Results • Performance measure The efficiency of retrieval proposed by Kankanhalli N: the number of ground truth T: the first T similar image we consider in retrieval n: the number of matched images in retrieval

  18. Significance maps SPIHT-autocorrelogram T 5 10 15 20 5 10 15 20 Efficiency 0.998 0.773 0.779 0.783 0.994 0.867 0.882 0.897 Experimental Results • Results the performance between significance maps and SPIHT-autocorrelogram

  19. SPIHT-autocorrelograms Significance maps rank rank image image L1 distance L1 distance 1 1 0.jpg 0.jpg 0 0 2 2 A1_0.jpg A1_0.jpg 1335 62 3 A4_0.jpg 79 3 A3_0.jpg 1772 4 A3_0.jpg 84 4 A4_0.jpg 1882 5 A2_0.jpg 236 5 A7_0.jpg 2627 6 A6_0.jpg 484 7 A5_0.jpg 599 6 A2_0.jpg 3258 8 A6_6.jpg 836 7 A8_0.jpg 3641 9 A4_12.jpg 849 8 A5_0.jpg 3843 10 A5_12.jpg 851 9 A6_0.jpg 4660 11 A4_7.jpg 852 10 A5_1.jpg 7486 12 A5_6.jpg 853 11 A6_1.jpg 7758 12 A2_1.jpg 7976 Experimental Results • Results an example: query by 0.jpg

  20. Future work • More attack modes • Reading more papers • Comparing with papers

More Related