Retrieval of the Ornaments from the Hand-Press Period: an Overview
Etienne Baudrier LSIIT (Illkirch, France)
Sébastien Busson CESR (Tours, France)
Silvio Corsini BCU (Lausanne, Switzerland)
Mathieu Delalandre CVC (Barcelona, Spain)
Jérôme Landré CReSTIC (Troyes, France)
Frédéric Morain-Nicolier CReSTIC (Troyes, France)
Plan
• About this work …
• Hand Press Period
• About Ornaments
• Digital Collection of Ornaments
• How DIA can help ?
• Content Based Image Retrieval
• Visual Comparison
• Conclusions and Perspectives
About this work …
Computer Science People
1. Etienne Baudrier2. Mickael Coustaty3. Mathieu Delalandre 4. Nathalie Girard5. Nicholas Journet6. Dimosthenis Karatzas7. Jerome Landré8. Kamel Ait-Mohand9. Jean-Marc Ogier10.Nicolas Ragot11.Jean-Yves Ramel
Human Science People
1. Pierre Aquilon2. Sébastien Busson3. Silvio Corsini4. Marie-Luce Demonet5. Stephen Rawles 6. Toshinori Uetani
One-day Workshop13th November 2007
CESR, Tours city, France
CESR
Labs of Human ScienceLabs of Computer Science
Hand Press Period (1/2)
The Hand-Press period runs from around 1454 (approximate date of Gutenberg’s invention) tothrough the first half of the nineteenth century (when mechanized presses started to appear).
a hand-press book
1454Gutenberg
half 18th
mechanized presses
Hand Press
hand press character matrix
Hand Press Period (2/2)
HPB Databasehttp://www.cerl.org/ 22 European libraries1450 - half 19th
3 Millions books
Trinity old library (Dublin, Ireland)16th - today
Mathematics, medicine, history, music, religion, literature, etc.
About Ornaments (1/2)
Ornaments in pages
“lettrine”
“fleuron”
to start a paragraph
trademark of a printing house
“cul de lampe”
to close a part or a chapter
to epitomize a concept , or to represent a person, such as a king or saint.
“emblème”
Categories of ornaments
ornaments text
About Ornaments (2/2)
Page 3,4 ornaments/page
Book 103,4 ornaments/book
Foreground pixels [Journet’05]
Text 63%
Graphics 37%
Part of ornaments in books(BVH dataset, 46 books)
sciences, medical, religion …
Hand Press books are composed for a large part of ornaments.
Pictures were a powerful mean of communication at this period due to the low education level of people.
Digital Collections of Ornaments (1/2)
25H112 rocks
44G312 prisoner; in fetters
91E461 punishment of Prometheus; he is chained to a rock, usually by Vulcan and/or Mercury
91E4611 an eagle tears at Prometheus' liver
DigitalizationPre-processing
(deskew, lighting correction, filtering, cropping…)
Layout analysisand segmentation
[Ramel’07]
Expert Classification using thesaurus
icon class encoding of an emblem image
Digital Collections of Ornaments (2/2)
DLs Size Periods Web links
BVH 14 000 16th http://www.bvh.univ-tours.fr
Fleuron 6 600 17th http://dbserv1-bcu.unil.ch/ornements/scripts/
Impact 2 200 16th-18th http://eclipsi.bib.ub.es/imp/impcat.htm
Mouriau 1 850 18th http://www.ornements-typo-mouriau.be/
Moriane 1 500 18th http://promethee.philo.ulg.ac.be/moriane/ornSearch.aspx
26 150
Collections of ornaments are small in regard to mass digitalization collections (e.g. Million Book Project), two main reasons:
(1)Mass digitalization projects are thought in terms of OCR only (layout analysis aims to perform text/graphics separation, final electronic documents are “ASCII code”, no use of high-level document model)
Digitalization programs should consider better the graphics aspects.
(2)Classification using thesaurus by human experts is time consuming (15-20 mn per image) Collaborative platforms, integrating DIA components, can help in.
Other smallest datasets are ArtDico, Canadian heraldry, Printers' Devices, etc.
How DIA can help ? (1/2)
A duplicated block
Redundancy of ornaments in books
A same block used in 2 books
Vascosan 1555 Marnef 1576Printing house
tamponexchange
copy
1531-1548
1511-1542
1555-1578
1497-1507
Tracking of plugs noise
offset
precision
skewing
scaling
scalability,mass of data
weak resolution,lossy compression
How DIA can help ? (2/2)
DB1
DB2
CBIR
DBn
---
Query image
Visual Comparison
R1
R2
R3
Context informationPublication datesPublication placesPractices of printers…
submita query
retrievalresults
comparison
visualization
assign previous classification
Meta
Meta
Meta
Meta
Digital CollectionsOf ornaments
Content Based Image Retrieval
Ideal methodHigh precision (weak difference)Robust (noise, skew, offset)Invariant to scaleFast comparison (online, mass of data)Scalable
Bigun’96
Chen’03
w
h
h w
Radiogram 0° Radiogram 90°
Detection of key points
(Haris)
Zernike moments
(local template)
Nearest points compared with a
likelihood estimation
Baudrier’08
Expert set resolution analysis
Hausdorff distance
between images
SVM classification
Delalandre’07
Run Length Encoding
Histogram centering
RLE Comparison
Orientation Radiograms
Fourier Descriptors
Euclidean Distance Comparison
Visual Comparison
Ideal methodHighlight pertinent differencesMake an hypothesis of relative datingInvariant to scaleRobust (noise, skew, offset)
Beusekom’07
Detection of points of interest (connected
components)
Pixel to Pixel Difference Map
(PPDMap)
PPDMapBlockA#1 LDMapBlockA#2
Baudrier’07
Equivalent ellipse computation
(first image moments)
Local Dissimilarity Map (LDMap)
Image Registration
Visualization Method
Conclusions and Perspectives
Large ornament material is available, but there is few digital collections Digitalization programs should consider better the graphics aspects. Collaborative platforms, integrating DIA components, can help in. Two database levels (with, without thesaurus classification)
DIA components CBIR systems (orientation signature, points of interest, image distance, compressed
representation) Lack of evaluation of the methods make difficult the comparison To define benchmark datasets (time, precision/recall) Methods propose a tradeoff between complexity/precision, possible combination
Visual Comparison (registration, PPDMap, LDMap) Hard point is the registration, user interaction could help in