1. Fast System for the Retrieval of Ornamental Letter Image M. Delalandre 1, J.M. Ogier 2, J. Lladós 1 1 CVC, Barcelona, Spain 2 L3i, La Rochelle, France L3i Meeting L3i, La Rochelle, France Thursday 13th July 2007

2. Plan Introduction Our System Works in Progress Conclusions and Perspectives

3. Introduction (1/3) Old Printed Graphics Old books of XV° and XVI° centuries Bartolomeo (1534)Alciati (1511)Laurens (1621) figure ornamental letter headline 41% ornamental letter 59% others Graphics type 63% textual 37% graphical Foreground pixel [Jour’05] 4755 (3.4 per page)Graphics 1385Page 46Book Graphical parts CESR Database 1. Old printed graphics 2. Needs of Historian People 3. Problematic & Approaches

4. Vascosan 1555Marnef 1576 (1) Wood plug tracking Printing house plug exchange copy 1531-1548 1511-1542 1555-1578 1497-1507 Introduction (2/3) Needs of Historian People (2) User-driven historical metadata acquisition Metadata file Metadata file Metadata file Metadata file Without retrieval With retrieval more faster reduce error Wood plug (bottom view) Retrieve similar printings Plug 1 Plug 2 Plug 3 Printing 1 Printing 2 Meeting with people of CESR (Tours) 1. Old printed graphics 2. Needs of Historian People 3. Problematic & Approaches

5. Introduction (3/3) Problematic & Approaches DB query results result Noise Offset Shape complexity Accuracy Scalability several hundred of classes several thousand of images Real-time Problematic 1. Old printed graphics 2. Needs of Historian People 3. Problematic & Approaches Existing Approaches descriptors Fast Accurate To scalar [Loncaric’98]  Hough, Radon, Zernike, Hu, Fourrier  scaled and orientation invariant  fast  local (character, symbol, digit) To image [Gesu’99]  template matching, Hausdorff distance  no scaled and orientation invariant  slow  global (scene) Template matching versus scalar descriptors Accuracy and low complexity are needed for our problematic, Key idea: to work on a compressed representation of image Key Idea

6. Plan Introduction Our System Works in Progress Conclusions and Perspectives

7. Our System (1/4) System Overview Query Compression Centering and Comparison R1 R2 R3 Checking Image Database

8. Our System (2/4) Checking Compression Centering and Comparison Checking Several image providers Several digitalization tools Long time process Human supervised Complex plate-form … Digitalization problems 250 to 350Resolutions UncompressCompression TiffFormats grayModel 279.7 MpSize 2038Files Results QUEID Engine Base accepted rejected Parameters Checking

9. Our System (3/4) Compression Compression image foreground background both 0.75 0.95 0.88 Results Compression Centering and Comparison Filtering

10. Centering x2x2 x2x2 x2x2 x1x1 x1x1 x1x1 x2x2 x2x2 x1x1 line (y) image 1 line (y+d y ) image 2 x stack reference while x 2  x 1 handle image 2 while x 1  x 2 handle image 1 Comparison Our System (4/4) Centering and Comparison Compression Centering and Comparison Filtering Results 903.62600.8Max 337.06137.7Mean 176.677.74Min Time s Size k.pixel 137.0687.8Max 41.6815.5Mean 22.321.1Min Time s Size k.run image database query image 7 times faster

11. Plan Introduction Our System Works in Progress Conclusions and Perspectives

12. Mean query of 40 s, how to reduce again without using a lossless compression and to loose accuracy ? Level 1 : image sizes Level 2 : black, white pixels Level 3 : RLE comparison Our first system Works in Progress (1/3) How to process the distance curve ? 11 22 if  1 -  2 < 0 push x, cluster while  1 -  2 < 0 next Using a basic clustering algorithm ‘elbow criteria’ query 1 st Level 2 sd Level To use a system approach using different level of operator (from more speed to more accurate) to select image to compare Our key idea Speed Depth

13. From 4% to 59%, how to reduce the variability ? To work on a better selection criteria seems ambiguous … Works in Progress (2/3) To add an intermediate operator between scalar and image data Our key idea Selection results 59%Max 24%Mean 4%Min Selection % 4 times faster

14. Base IHM Retrieve engine control display retrieve Labels driven labelling Bench1Bench2 To produce Example of query result 0.1947 0.2517 0.3485 0.3616 0.3819 0.4064 Same plug Next plug Query 0.4109 0.4209 First results seem good, but how to get the ground truth and to evaluate our system? Works in Progress (3/3) To use our engine to produce benchmark database Our key idea

15. Plan Introduction Our System Works in Progress Conclusions and Perspectives

16. Conclusions QUEID to check image database Speedup of comparison (  30 times faster) RLE compression (   7 times faster) Image selection (   4 times faster) Perspectives To add operator to reduce the variability of selection process and to speed again the process To extend our system to do performance evaluation Conclusions and Perspectives