delalandre2006b

A Fast System for Dropcap Image Retrieval: 

A Fast System for Dropcap Image Retrieval Mathieu Delalandre and Jean-Marc Ogier L3i, La Rochelle University, France mathieu.delalandre@univ-lr.fr

Short CV: 

Short CV

Short CV: 

Short CV mais aussi des bandeaux, portraits, armoiries, fleurons, marques … Personal Information Mathieu Delalandre, 32 years old, Married Academic Degrees 1995-1998 Lic.Sc In Industrial Computing Rouen University, France 1998-2001 M.Sc in Computer Science Rouen University, France Research Experiences (5 years, Graphics Recognition) 04/01-09/01 Master PSI Laboratory (Rouen, France) 10/01-04/05 PhD PSI Laboratory (Rouen, France) 05/05-09/05 Post-doc SCSIT (Nottingham, England) 10/05-10/06 Post-doc L3i Laboratory (La Rochelle, France) 11/06-12/06 Post-doc PSI Laboratory (Rouen, France) 01/07-12/09 Post-doc CVC (Barcelone, Spain)

Introduction: 

Introduction - Old books - Old graphics retrieval - Our problem

IntroductionOld books: 

Introduction Old books Old books of XV° and XVI° centuries Samples Example of digitized database (BVH, CESR Tours) Old Graphics Old books - Old graphics retrieval Our problem

IntroductionOld graphics retrieval: 

Introduction Old graphics retrieval Old books - Old graphics retrieval Our problem 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 Retrieval criterion

IntroductionOur problem (1/2): 

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) Wood Plug Tracking Old books - Old graphics retrieval Our problem

IntroductionOur problem (2/2): 

Introduction Our problem (2/2) Problem features No scaled, no oriented Noise Offset Complexity Accuracy Scalability 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) Old books - Old graphics retrieval Our problem

Our system: 

Our system

Our systemFormatting: 

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 » OLDB (Ornamental Letters Database) Before (oldb.jpg) After

Our systemCompression: 

Our system Compression Run based compression Run Length Encoding (RLE) Compression rate RLE Types OLDB results Fixed threshold binarisation Both RLE

Our systemCentering and comparison: 

Our system Centering and comparison Centering while x2  x1 handle image 2 while x1  x2 handle image 1 OLDB results Comparison

In progress: 

In progress

In progress: 

In progress Our problem Current time :  40 s Wished time : < 4 s To use a lossless compression 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 if 1 - 2 < 0 push x, cluster while 1 - 2 < 0 next

In progress: 

In progress OLDB results To decrease variability To work on selection To add a level Run based signature

In progress: 

In progress Query example Performance evaluation Criterion ? Scalability Accuracy Time processing Benchmark system

Conclusions and perspectives: 

Conclusions and perspectives

Conclusions et perspectives: 

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

Bibliography: 

Bibliography

Bibliography: 

Bibliography 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. V. D. Gesu and V. Starovoitov. Distance based function for image comparison. Pattern Recognition Letters (PRL), 20(2):207-214, 1999. S. Loncaric. A survey of shape analysis techniques. Pattern Recognition (PR), 31(8):983-1001, 1998. R. Pareti and N. Vincent. Global discrimination of graphics styles. In Workshop on Graphics Recognition (GREC), pages 120-128, 2005. 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. 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.

Thanks …: 

Thanks …