CS 545I - Advanced Image Databases, W 95/96
Friday, 13 Jan 1996. First lecture CS545I
1 - Why study image databases?
Image databases are an increasingly important type of database as
sources of images increase, methods of storage improve, and the Net offers
the communication ability.
However, images have important unique characteristics.
The database designer must know and understand these characteristics.
2- Goal of seminar
To gain an appreciation of the special problems of image retrieval, to learn
about some current systems, to learn about the indexing and database
organization technqiues, and to get hands-on experience with one of the
3 - Some sources of images
Medical imagery (x-ray and CR, NMR, ultrasonic, etc,)
News and entertainment videos
Art and photo collections
Consumer and engineering catalogues
Scientific images (astronomy, earth resources, etc.)
4 - Types of user request
There is a remarkable variety of user requests. Users may want combinations
of the following requests:
SIMILALARITY: Find image that looks like this image (or
parts of it look like part of this image)
OBJECT: Find image that contains a cat
OBJECT RELATIONSHIP: Find image that contains a cat near a dog
MOOD: Find a sad/happy/... picture
VIEW ANGLE: Find a picture of a crowd taken from an airplane
TIME OF DAY/SEASON: Find a picture of Yosemite taken at
COLOR: Find a picture with a red apple
TEXTURE: Find picture with a brick texture
SHAPE: Find picture with circular object
GEOGRAPHIC: Find aerial image of the port of San Francisco
5 - Approach to image database design
One could treat an image database as if it were a document database.
Create a database in which each image is manually tagged with
an index term
Queries are combinations of index terms.
User reviews results and modifies query
This approach does not take advantage of the special aspects of an image:
People can analyze images very fast
An image has many "meanings" depending on the interest or
"point of view" of the viewer
6 - An Ideal image database system:
- allows user to review image "thumbnails"
- presents image in order of "closeness"
- uses a variety of description approaches, many of them
- also allows retrieval using conventional relational, etc.
7 - Capabilities needed
- Similarity metric must match human idea of similarity of images
- Search must be efficient enough to be interactive
- User must be able to specify needs without becoming an image or DB
expert -- a good approach is for user to specify by providing examples
close to what is desired
- Image descriptor-finding must be automated
8 - Problems
How to normalize an image: scale, orientation
Capturing aspects of content by using invariants or discriminants
Can these invariants retain semantic info?
Efficiency of invariants
Can title or caption of image aid in finding invariants?
9 - Some existing systems
IBM Ultimedia manager combines QBIC (Query by Image Content) color, shape,
texture and traditional DB searching
MIT Media lab Photobook that uses appearance, 2-D shape, and texture for
interactive image browsing
UC Berkeley Chabot system integrates relational DB and color analysis.