SESSION TWO
Using stuff
Rough Guide to Image Management
CILIP, 31 March 2010
SESSION TWO
Using stuff
Metadata content and ontologies:
requirements for effective retrieval
Rough Guide to Image Management
CILIP, 31 March 2010
Create metadata
Rough Guide to Image Management
CILIP, 31 March 2010
Create metadata
© Radio Times
Rough Guide to Image Management
CILIP, 31 March 2010
Metadata needs:
‘Bibliographic’ description: creator, title,
subject etc
Format details
Relationships, source
Context, language
Rights
Technical data
Standards
Rough Guide to Image Management
CILIP, 31 March 2010
Standards
For a convenient listing see http://metadata.net/
DCMI: Dublin Core Metadata Initiative
http://dublincore.org/
MODS: Metadata Object Description Schema
http://www.loc.gov/standards/mods/
METS: Metadata Encoding and Transmission
Schema http://www.loc.gov/standards/mets/
RDF: Resource Description Framework
http://www.w3.org/RDF/
Rough Guide to Image Management
CILIP, 31 March 2010
Why bother?
Machine indexing of texts is advanced and
quite efficient
Not so for pictures: where
meaning/significance is often attributed by
context
E.g. ‘the first computer’, ‘the last man on the
moon’
Context must be described in metadata
Rough Guide to Image Management
CILIP, 31 March 2010
Ontologies
Ontologies provide a way of defining
context
A three-dimensional thesaurus
If we need words, we need definitions of
words
Especially in multiple languages
Rough Guide to Image Management
CILIP, 31 March 2010
Getting started with ontologies
Useful page from AI Topics:
http://www.aaai.org/AITopics/html/ontol.html
Marine Metadata Interoperability
http://marinemetadata.org/guides/vocabs/ont/definition
Gives comprehensive guidance on using
ontologies and related tools, applicable beyond
the marine domain
Rough Guide to Image Management
CILIP, 31 March 2010
Getting started with ontologies
http://www.aaai.org/AITopics/html/ontol.html
Rough Guide to Image Management
CILIP, 31 March 2010
http://marinemetadata.org/guides/vocabs/ont/definition
Rough Guide to Image Management
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Finding ontologies and tools
Swoogle
http://swoogle.umbc.edu/
Domain-specific e.g. FAO Agricultural
Information Management Standards
(AIMS)
http://aims.fao.org/pages/377/sub
Rough Guide to Image Management
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http://swoogle.umbc.edu/
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http://aims.fao.org/pages/377/sub
Rough Guide to Image Management
CILIP, 31 March 2010
Linguistic tools
ULAN: Union List of Artist’s Names Online
http://www.getty.edu/research/conducting_resea
rch/vocabularies/ulan/
TGN: Thesaurus of Geographic Names Online
http://www.getty.edu/research/conducting_resea
rch/vocabularies/tgn/
AAT: Art & Architecture Thesaurus Online
http://www.getty.edu/research/conducting_resea
rch/vocabularies/aat/
ICONCLASS http://www.iconclass.nl/
WORDNET http://wordnet.princeton.edu/
Rough Guide to Image Management
CILIP, 31 March 2010
Content-based Image Retrieval
Automatic analysis of colour distribution
and shapes
Edge detection to determine shape
Rough Guide to Image Management
CILIP, 31 March 2010
Just how big is the ‘semantic gap’?


To what extent is it now possible for computers to identify objects within
images by direct inspection of the pixel information?
The results I am about to show you are from two state-of-the-art
automated methods for
 object detection
 semantic segmentation

Independently they produce good results, and in combination they are
remarkable

Credits: Jamie Shotton (2007) Contour and
Texture for Visual Recognition of Object
Categories.
Ph. D. Thesis, University of
Cambridge
Object detection using contour fragments



These results are obtained using the first method, based upon contour
fragments, used here to detect the presence of horses in images
The algorithm has been ‘educated’ using a set of training images, and has
then been let loose on these and other test images, which it has analysed
automatically
On the left of each pair, the green boxes surround the detected horses,
while on the right the contour fragments used in the detection are shown
This method works well on a variety of objects


It gives few false positives and few false negatives, with almost
perfect results for motorbikes and cows!
However, it does require training, and has not yet been tested on
biological research images
Automatic image segmentation using texture

The second method combines texture, colour, shape and context

It learns from a set of 591 training images pre-labelled for 21 object classes
Results of the ‘texture’ method

Results of the ‘texture’ method for the semantic segmentation of test
images
building
car
road
grass
grass
sheep
building
water
cow
cat
road
sky
book
flower
bicycle
road
building
sign
grass
cow
chair
grass
. . . .but the method is not perfect
sky
tree
cow
grass

building
dog
sign
water
road
road
road
sky
bike
building
As Jamie says in his conclusion, concerning the capabilities of machine vision:
“While we are still a considerable way from accurately recognizing the tens
of thousands of classes that humans effortlessly distinguish, despite
incredible variations in appearance, we believe that this thesis has taken a
positive step towards a solution”

So the semantic gap between the capabilities of machine vision and the
necessity for human metadata annotation is perhaps not as wide as I made
out initially!
Content-based Video Retrieval
Works better: moving objects easier to anaylse
Broadcasting systems use audio stream to help
index video
Informedia Digital Video Library
http://www.informedia.cs.cmu.edu/
“combines speech, image and natural language
understanding to automatically transcribe,
segment and index linear video for intelligent
search and image retrieval”
Rough Guide to Image Management
CILIP, 31 March 2010
Rough Guide to Image Management
CILIP, 31 March 2010
SESSION TWO
Using stuff
Format and delivery issues
Rough Guide to Image Management
CILIP, 31 March 2010
There’s no such thing as a digital
image!
Digital images are just a stream of 1’s and
0’s
They have to be processed to be seen
Almost all processing degrades the image
How much degradation is acceptable?
Rough Guide to Image Management
CILIP, 31 March 2010
Typical formats
RAW : unprocessed, exactly as captured
by camera.
TIFF : processed but uncompressed.
Generally best for archiving
JPEG : processed and compressed. Best
for ‘working’ copies, usually OK for web,
not always for publication
Rough Guide to Image Management
CILIP, 31 March 2010
How big do you want it?
DPI no guide to quality: depends on size of
original and size of output. Better to quote size in
pixels
Output size depends on resolution of output
device
An image that is 1000 × 800 pixels
On an old 72ppi monitor will view at 13.9” × 11.1”
On a new 96ppi monitor will view at 10.4” × 8.3”
On an average inkjet (150lpi) will print at 6.6” × 5.3”
On a high quality printer (250lpi) will print at 4” × 3.2”
No. of pixels ÷ Output resolution = Output size
(http://www.jiscdigitalmedia.ac.uk/stillimages/advice/do-digital-images-existin-the-real-world/)
Rough Guide to Image Management
CILIP, 31 March 2010
Choosing a file format
Archive highest quality – generally TIFF
Use working copies – generally JPEG –
for display
PDF or PSD may be appropriate for some
projects
 see
http://www.jiscdigitalmedia.ac.uk/stillimages/advi
ce/choosing-a-file-format-for-digital-still-images/
Rough Guide to Image Management
CILIP, 31 March 2010
Delivering to the end user
Low-res JPEGs ok for web or PowerPoint
High-res JPEGs normally needed for
publication
Author’s responsibility to check publisher’s
requirements
Normally chargeable – plus reproduction
rights
To keep or not to keep a library copy?
Rough Guide to Image Management
CILIP, 31 March 2010
If you keep a copy…
Needs long-term storage
Needs adequate metadata
May need additional scanning to create
logical unit
… so needs institutional policy decision
Rough Guide to Image Management
CILIP, 31 March 2010
SESSION TWO
Using stuff
Rights issues and commercial factors
Rough Guide to Image Management
CILIP, 31 March 2010
Copyright in images
Photographs and images are protected as
artistic works, provided original and ‘fixed’
This right does not need to be stated
Electronic/digital copyright not specifically
mentioned in law, which lags behind technology
Ease of copying and conversion makes
infringement easy; permission given for one
format may not apply to another
Rough Guide to Image Management
CILIP, 31 March 2010
Who has the rights?
The creator of the image
The creator of the object imaged
The subject of the image
Rough Guide to Image Management
CILIP, 31 March 2010
Don’t do it!
The Internet is NOT a copyright-free zone
DO seek copyright permission
DO acknowledge the source
DON’T alter the image
Paul Pedley, Copyright and images, Library and
Information Update, 6(6) May 2007, 36-37
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Fair dealing
You may use images for private study and
NON-COMMERCIAL research
But not on websites OR INTRANETS
because equivalent to multiple copying
Permission must always be sought for that
Establishing the copyright owner can be
extremely difficult
Rough Guide to Image Management
CILIP, 31 March 2010
Gowers proposals
Gowers Review of Intellectual Property
HM Treasury, The Stationery Office, 2006
Proposes provision for ‘orphan works’ where
copyright owner cannot be traced
Intellectual Property Office [=Patent Office]
should issue guidance on parameters of
‘reasonable search’
And establish a voluntary register of copyright
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Rough Guide to Image Management
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Rough Guide to Image Management
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How long?
70 years after death of photographer (if
UK citizen) for photos taken after August
1989; earlier, can be longer or shorter
Take advice!
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Rough Guide to Image Management
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Open Access
Creative Commons
http://creativecommons.org/
Creative Archive (BBC)
http://creativearchive.bbc.co.uk/
Science Commons
http://sciencecommons.org/
All offer opportunity for creators to license
material for web use: non-commercial,
credited, share-alike
Rough Guide to Image Management
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Rough Guide to Image Management
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Rough Guide to Image Management
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Rough Guide to Image Management
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More info
JISC Digital Media:
http://www.jiscdigitalmedia.ac.uk/stillimages/adv
ice/copyright-and-digital-images/
Rough Guide to Image Management
CILIP, 31 March 2010
Pricing your own material
No standard guidelines
Reproduction fees vary widely
V&A (http://www.vam.ac.uk/resources/buying/)
often taken as ‘best practice’: now scrapped
repro fees for scholarly publications
Remember quoted prices are maxima – may be
discounted or waived
Administration is costly
Remember original aim of digitising
Rough Guide to Image Management
CILIP, 31 March 2010
Rough Guide to Image Management
CILIP, 31 March 2010
Buying material
Unless for library collection, best for
enquirer to deal direct with source
May need advice on format, type of rights
required etc
For library retention use highest quality
possible
Rough Guide to Image Management
CILIP, 31 March 2010