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A Digital Image Watermarking Method Based on Labeled Bisecting Clustering Algorithm

A Digital Image Watermarking Method Based on Labeled Bisecting Clustering Algorithm. Source : IEICE Transactions on Fundamentals, VOL.E87-A,NO.1 JANUARY 2004 pp.282-285 Authors : Shu-Chuan CHU, John F. RODDICK, Zhe-Ming LU and Jeng-Shyang PAN

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A Digital Image Watermarking Method Based on Labeled Bisecting Clustering Algorithm

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  1. A Digital Image Watermarking Method Based on Labeled Bisecting Clustering Algorithm Source: IEICE Transactions on Fundamentals, VOL.E87-A,NO.1 JANUARY 2004 pp.282-285 Authors:Shu-Chuan CHU, John F. RODDICK, Zhe-Ming LU and Jeng-Shyang PAN Speaker:Chuen-Ko Tsai Date:2004/05/12

  2. Outline • Introduction • The Watermarking Algorithm • Experimental Results

  3. Introduction Privacy key Original image Embedded image Embedding algorithm Watermark Watermark Extracting algorithm Privacy key

  4. A labeled bisecting clustering algorithm • Step 1:The whole training set is viewed a single cluster. Split this cluster into two sub-clusters. One is labeled ‘0’, the other is labeled ‘1’. • Step 2:Pick the cluster Cp that has the largest distortion to split. • Step 3:Find 2 sub-clusters using the basic LGB algorithm (Bitsecting step). • Step 4:Repeat Step 3 Im times and take the split that produces the clustering with the highest overall similarity. Thus, we can obtain two new clusters Ca and Cb.

  5. A labeled bisecting clustering algorithm (cont.) • Step 5:For cluster Ca and Cb, find their neighboring clusters other than each other. If Ca has a nearest neighboring cluster Cc labeled l, and Cb has no neighboring clusters, the Ca is labeled 1-l and Cb is labeled l. Otherwise, if Ca has a nearest neighboring clustering Cc labeled l, and Cb also has a nearest neighboring cluster Cdlabeled m, then Cais labeled 1-l and Cbis labeled 1-m.

  6. A labeled bisecting clustering algorithm (cont.) • Step 6:Repeat steps 2, 3, 4, 5 until the desired number of clusters is reached. • Step 7:Record all cluster labels and centers to form the labeling key Keyl and the final codebook C, respectively.

  7. w h The Original image and watermark The binary watermark image of size 128*128 256-grayscale Lena image of size 512*512

  8. Step 1 Step 2 The whole training set is viewed as a single cluster Cp 0 1 The step for generate the codeword-labeled VQ codebook

  9. Step 3 Ca 0 1 0 1 Cp Cb The step for generate the codeword-labeled VQ codebook (cont.) Step 4 Find 2 sub-clusters using the basic LGB algorithm

  10. Step 5 Cc Ca 0 1 Cb The step for generate the codeword-labeled VQ codebook (cont.) Step 6 Repeat steps 2, 3, 4 and 5 until the desired number of clusters is reached Step 7 Record all cluster labels and centers to form the labeling key Keyl and the final codebook C, respectively 0

  11. A example to describe the embedding process for each input vector

  12. Experimental Results

  13. Experimental Results (cont.)

  14. Experimental Results (cont.)

  15. Experimental Results (cont.)

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