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Data Hiding Approach for Efficient Image Indexing

Data Hiding Approach for Efficient Image Indexing. J. Jiang and A. Armstrong, IEE ELECTRONICS LETTERS , Vol. 38, No. 23, November 2002, pp.1424-1425. Advisor ： Dr. Chang, Chin-Chen Reporter： Lee, Jiau-Yun Date ： 2003/3/4. Outline. Introduction Overall System Algorithm Design

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Data Hiding Approach for Efficient Image Indexing

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  1. Data Hiding Approach for Efficient Image Indexing J. Jiang and A. Armstrong, IEE ELECTRONICS LETTERS, Vol. 38, No. 23, November 2002, pp.1424-1425 Advisor ：Dr. Chang, Chin-Chen Reporter：Lee, Jiau-Yun Date ：2003/3/4

  2. Outline • Introduction • Overall System • Algorithm Design • Experimental Results • Conclusions

  3. Introduction • Content based image indexing and retrieval has attracted. • Those indexing keys are often ignored(negligible). • When millions of images are stored, the storage space becomes significant.

  4. Overall System Texture extraction Store to DB Indexing key hiding Entropy coding DCT transform Quantize Input image

  5. Algorithm Design • Extract LBP-based texture key. • DCT transformation. • Quantize the DCT coefficients. • Alone the zigzag scanning route • To choose the embedding position. • Hiding procedure.

  6. Local Bit Partition(LBP) • Step 1: (11110100)=244 • Step 2:

  7. Step 3: Local Bit Partition(count.) Texture key (00001011) (00100011) ………. (01001000) ………. ……….

  8. LBP-based texture key • For each pixel p in the image, the eight neighbors are examined to see if their intensity is greater than that of p • x is the pixel being examined • yi is the surrounding pixels(b7b6…b0)

  9. LBP-based texture key(count.) • A histogram of these numbers is used to represent the texture of the image. : the elements of the histogram : the counting operator, which records the number of times that has occurred.

  10. Zigzag Scanning

  11. Hiding procedure • If we want hide four bits(message) inside the four coefficients of each blocks. • X={x0,x1,x2,x3} • D={d0,d1,d2,d3} • For(I=0 to 3){ If (di is odd and xi=0) di=di+1; else (di is even and xi=1) di=di+1;}

  12. Experimental Results • A small database of around 10000 images in BMP format was tested.

  13. Experimental Results(count.) • The storage cost for indexing key is 2.5mb for 10000 images.

  14. Conclusions • The storage space of our method is reduced to the region of 7-21%. • The quality of reconstruction remains almost unchanged. • The data hiding approach could affect efficiency.

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