Contact: Remco Veltkamp
In our multimedia indexing research we focus on shape-based retrieval. However,
shape matching is often computationally more
demanding than matching color or texture features. So for large multimedia
databases, there must be an index to avoid shape matching for all objects in
the database. Our current research is on indexnig via vantage objects.
The idea is to calculate the distance between all database objects and a
vantage object. The set of objects that have about the same distance to
the vantage as a query object, contains also those objects that have about
the same to the query object (if the triangle inequality holds).
This can be extended to more vantage objects:
In this way, complex shape comparisons need only be done with a few vantage
objects, at the cost of false positives, but no false negatives.
After computing the distances of all database objects to a fixed number of
vantage objects, for querying only a few expensive shape comparisons are
needed, the actual range query is done efficiently in higher dimensional
The objective of the MINDSHADE project is to approach the following
three fundamental issues in image retrieval:
The approach that we take for reaching the above objectives is to
perform a decomposition of the shapes that are present in the
images. The decomposition of the shape is a union of primitives such
as polylines of fixed length, circular arcs, triangles, discs,
etc., that approximates the shape. Research issues are the
design of algorithms to efficiently find a decomposition among the
exponential number of possibilities, and to investigate different
- Given a query image and an image from the collection,
determine how similar the present shapes are, and which
transformation of the query image shapes would minimize the
- Given a large collection of images, build a data structure to
efficiently search for those image shapes similar to shapes in the
- Relevance feedback
- Given a selection from the collection, with positive and
negative assessments from the user, find images whose shapes are
more similar to the positively labeled images, and more dissimilar
to the negatively labeled images.
Smurf provides an experimentation platform for content-based image
retrieval. It allows us to experiment with different types of
features, matching algorithms, indexing structures, and user
interfaces. The architecture of the framework is as follows:
In this project we have developed the indexing through vantage objects, see
the general description above.
We have applied this indexing scheme to for example hieroglyphic retrieval,
with the following vantage object (polyline that occur in the collection of
Below is a query with a polyline, and some retrieved hieroglyphics that
contain the query:
The following people are involved in this research area:
For implementation and testing we have a
available with a 190 Gigabyte file server, a web server, and modern top-end
PC workstations with stereo viewing facilities.
This lab was partially donated by Microsoft and the Dutch Science Foundation.
There are ample opportunities for students to do master projects in this area.
The list above gives a number of research themes. The projects can both be
theoretically oriented and more experimental in nature.
The lab is available for the students to work in. If you are interested, contact
For more information see the
webmaster: Remco Veltkamp