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Madonne Talk (Tours University) 7 th November 2006 A Fast System for Dropcap Image Retrieval Mathieu Delalandre and Jean-Marc Ogier L3i, La Rochelle University,

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Presentation on theme: "Madonne Talk (Tours University) 7 th November 2006 A Fast System for Dropcap Image Retrieval Mathieu Delalandre and Jean-Marc Ogier L3i, La Rochelle University,"— Presentation transcript:

1 Madonne Talk (Tours University) 7 th November 2006 A Fast System for Dropcap Image Retrieval Mathieu Delalandre and Jean-Marc Ogier L3i, La Rochelle University, France mathieu.delalandre@univ-lr.fr

2 Madonne Talk (Tours University) 7 th November 2006 Short CV

3 Madonne Talk (Tours University) 7th November 2006 Short CV mais aussi des bandeaux, portraits, armoiries, fleurons, marques … Personal Information  Mathieu Delalandre, 32 years old, Married Academic Degrees  1995-1998Lic.Sc In Industrial Computing Rouen University, France  1998-2001M.Sc in Computer Science Rouen University, France Research Experiences (5 years, Graphics Recognition)  04/01-09/01Master PSI Laboratory (Rouen, France)  10/01-04/05PhDPSI Laboratory (Rouen, France)  05/05-09/05Post-docSCSIT (Nottingham, England)  10/05-10/06Post-docL3i Laboratory (La Rochelle, France)  11/06-12/06Post-docPSI Laboratory (Rouen, France)  01/07-12/09Post-docCVC (Barcelone, Spain)

4 Madonne Talk (Tours University) 7 th November 2006 Introduction - Old books - Old graphics retrieval - Our problem

5 Madonne Talk (Tours University) 7th November 2006 Introduction Old books Old books of XV° and XVI° centuries  Samples Bartolomeo (1534) Alciati (1511) Laurens (1621) figure dropcap headline  Example of digitized database (BVH, CESR Tours) Book46 Page1385 Graphics4755 (3.4/page) Foreground pixel 63% textual 37% graphical Graphics type41% dropcap 59% others  Old Graphics - Old books - Old graphics retrieval - Our problem

6 Madonne Talk (Tours University) 7th November 2006 Introduction Old graphics retrieval - Old books - Old graphics retrieval - Our problem Image Database Query ExtractionComparison Index Indexing Retrieval Manual Index System overview  General architecture  Samples Pareti’05 Graphics style Zip law Uttama’05 Document layout MST Baudrier’05 Sub image Hausdorff distance Bigun’96 Stroke image Radiogram orientation letter (c)topic (vegetal) pattern (cross)  Retrieval criterion

7 Madonne Talk (Tours University) 7th November 2006 Introduction Our problem (1/2) Context  MAsse de DOnnées issues de la Numérisation du patrimoiNE (MADONNE) Project  Bibliothèques Virtuelles Humanistes (BVH) du Centre d’Etudes Supérieures de la Renaissance (CESR) Class 1 Class 2 Class 3 printing Wood plug (bottom view) Vascosan 1555Marnef 1576 Wood Plug Tracking Printing house tampon exchange copy 1531-1548 1511-1542 1555-1578 1497-1507 - Old books - Old graphics retrieval - Our problem

8 Madonne Talk (Tours University) 7th November 2006 Introduction Our problem (2/2) Problem features  No scaled, no oriented  Noise  Offset  Complexity  Accuracy  Scalability descriptors fast local complex global Descriptor choice To scalar [Loncaric’98]  Hough, Radon, Zernike, Hu, Fourrier  Scaled and orientation invariant  fast  local To image [Gesu’99]  Template matching, Hausdorff distance  no scaled and orientation invariant  global (scene) Query Compression Centering and Comparison R1 R2 R3 Formatting Image Database - Old books - Old graphics retrieval - Our problem

9 Madonne Talk (Tours University) 7 th November 2006 Our system Compression Centering and Comparison Formatting

10 Madonne Talk (Tours University) 7th November 2006 Our system Formatting Digitalization problems [Lawrence’00]  Problem sources Several image providers Several digitalization tools Length of process Human supervised …  QUEID « QUery Engine on Image Database » Diagnostic Base Expertis e QUEID query charts analysis Format Compression Centering and Comparison Formatting OLDB (Ornamental Letters Database)  Before (oldb.jpg)oldb.jpg  After Packbits and JpegCompression ?; from 72 to 450 dpiResolutions Jpeg and TiffFormats gray and colourModel 377.7 MpSize 2803Files 250 to 350Resolutions UncompressCompression TiffFormats grayModel 279.7 MpSize 2038Files

11 Madonne Talk (Tours University) 7th November 2006 Our system Compression Run based compression  Run Length Encoding (RLE)  Compression rate  RLE Types image foreground background both OLDB results Fixed threshold binarisation Both RLE 0.75 0.95 0.88 Compression Centering and Comparison Formatting

12 Madonne Talk (Tours University) 7th November 2006 Our system Centering and comparison Centering x2x2 x2x2 x2x2 x1x1 x1x1 x1x1 x2x2 x2x2 x1x1 line (y) image 1 line (y+d y ) image 2 x stack pointeur while x 2  x 1 handle image 2 while x 1  x 2 handle image 1 OLDB 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 Comparison Compression Centering and Comparison Formatting image database query image

13 Madonne Talk (Tours University) 7 th November 2006 In progress

14 Madonne Talk (Tours University) 7th November 2006 In progress query 1 st Level 2 sd Level Our problem  Current time :  40 s  Wished time : < 4 s To use a lossless compressio n To use a system approach Key idea First system  Level 1 : image sizes  Level 2 : black, white pixels  Level 3 : RLE comparison Depth Speed Selection algorithm 11 22 if  1 -  2 < 0 push x, cluster while  1 -  2 < 0 next

15 Madonne Talk (Tours University) 7th November 2006 In progress OLDB results 59%Max 24%Mean 4%Min Depth % To decrease variability To work on selection To add a level Run based signature

16 Madonne Talk (Tours University) 7th November 2006 In progress Query example 0.1947 0.2517 0.3485 0.3616 0.3819 0.4064 Same plug Next plug Query 0.4109 0.4209 Performance evaluation Base IHM Retrieve engine control display retrieve Labels driven labelling Bench1Bench2 To produce Criterion ? - Scalability - Accuracy - Time processing Benchmar k system

17 Madonne Talk (Tours University) 7 th November 2006 Conclusions and perspectives

18 Madonne Talk (Tours University) 7th November 2006 Conclusions et perspectives Conclusions  Dropcap image retrieval « wood tracking »  Formatting image database (QUEID)  Fast approach, two features RLE comparison (  7 to  9) Top-down strategy (  2 to  20)  Results  10 s for 2000 images (300 Mo) Perspectives  Working on RLE signature  Benchmark system for performance evaluation

19 Madonne Talk (Tours University) 7 th November 2006 Bibliography

20 Madonne Talk (Tours University) 7th November 2006 Bibliography 1. J. Bigun, S. Bhattacharjee, and S. Michel. Orientation radiograms for image retrieval: An alternative to segmentation. In International Conference on Pattern Recognition (ICPR), volume 3, pages 346-350, 1996. 2. V. D. Gesu and V. Starovoitov. Distance based function for image comparison. Pattern Recognition Letters (PRL), 20(2):207-214, 1999. 3. S. Loncaric. A survey of shape analysis techniques. Pattern Recognition (PR), 31(8):983-1001, 1998. 4. R. Pareti and N. Vincent. Global discrimination of graphics styles. In Workshop on Graphics Recognition (GREC), pages 120-128, 2005. 5. S. Uttama, M. Hammoud, C. Garrido, P. Franco, and J. Ogier. Ancient graphic documents characterization. In Workshop on Graphics Recognition (GREC), pages 97-105, 2005. 6. E. Baudrier, G. Millon, F. Nicolier, and S. Ruan. A fast binary-image comparison method with local-dissimilarity quantification. In International Conference on Pattern Recognition (ICPR), volume 3, pages 216- 219, 2006.

21 Madonne Talk (Tours University) 7 th November 2006 Thanks …

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