| The research
described below is carried out by the subtheme Content-Based
Image Retrieval
within the CWI research theme
"Signals
and Images"
Subtheme coordinator:
Eric Pauwels
There is a pressing need for efficient information management and mining
of the huge quantities of image data that are routinely being amassed.
These data are potentially an extremely valuable source of information, but their value
is limited unless they can be effectively explored and retrieved,
and it is becoming increasingly clear that in order to be efficient,
data mining needs to be based on semantics. However, the extraction of
semantically rich meta-data from the computationally accessible "low-level" features
poses tremendous scientific challenges.
Indeed, retrieval of images based exclusively on a number of fixed and
global features is often too crude to produce satisfactory results.
What is required is some form of adaptive (i.e. data-driven)
description that captures whatever is salient in each individual
image. Furthermore, the user should be able to provide the system
with relevance feedback to expedite the navigation-process
through a large database.
This means that the search-engine should be equipped with an
inference-engine that is able to observe the user-interaction and
learn from it.
Given the above, we feel that there is both a need and an opportunity to
systematically incorporate machine learning into an integrated approach
to image data mining. Enriching image databases with
additional layers of automatically generated semantic meta-data as
well as with artificial intelligence to reason about these (meta)data,
seems the only conceivable way forward.
International Research Projects
Our research group is involved in the following European
research projects:
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FOUNDIT
(Coordinator)
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Content-based image retrieval (FP5)
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MUSCLE
(Coordinator)
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Multimedia Understanding
through Semantics, Computation and Learning
(FP6 Network of Excellence)
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BioVision |
Roadmap for the future of biometrics in Europe through to
2010 (FP5)
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BioSecure |
Biometrics for secure Authentication (FP6 Network of
Excellence)
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Europhlukes
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Computer-assisted Photo-identification of Ceteceans (FP5)
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Research Interests
Clustering for Image Segmentation
Eric Pauwels,
Joint work with Greet
Frederix, Katholieke
Universiteit Leuven, Belgium.
When characterising the visual
content of natural images, global descriptions are often to coarse to be
really useful. In order to obtain a more flexible description one
needs to locate regions of interest that can be characterised separately.
For this reason, we are working on robust segmentation algorithms that
are based on non-parametric clustering.
Multimodal integration
Eric Pauwels, Mark Huiskes
To achieve robust performance in multimedia applications
one is faced with the problem of how to integrate various sources of data
resulting from different perceptual modalities. These include general-purpose modalities
such as vision, audio/speech and text, and a wide variety of special-purpose
sensors (motion detectors, pressure, IR etc).
We are investigating statistical approaches to the integration problem, particularly aimed at
exploiting both redundancy and non-accidentality; additionally we are working on
bio-inspired systems using layered and agent-based approaches.
Applications are in sensor networks, multimedia understanding and multimodal
interface design.
Learning, visualisation and simulation in CBIR
Eric Pauwels, Mark Huiskes
Joint work with Geert
Caenen, Katholieke Universiteit Leuven, Belgium.
As image interpretation is user- and task-dependent, relevance feedback is required for
reaching an adaptive and interactive understanding of a user's wishes.
Based on a detailed analysis of the special structure of the relevance
feedback learning problem, taking into account for instance partial relevance
and feature saliency, we are designing new methods for statistical inference to
interpret the user feedback data.
In addition we are analyzing the performance of relevance feedback inference
methods by combining a new approach to feature simulation with realistic search scenarios.
Image segmentation and perceptual grouping
Eric Pauwels, Mark Huiskes
To reach an understanding of images in terms of their constituent parts,
low-level segmentation based on simple homogeneity and connectedness is generally not
sufficient. Additional mechanisms such as the famous Gestalt principles (e.g.
similarity and goodness-of-curve) and rules of saliency and
figure-ground organization play an important intermediary role in
going from signal to meaning.
To get a handle on the problem of integrating the diverse grouping principles,
we are currently investigating the feasibility of various agent-based solutions.
Interactive feature annotation and design
Eric Pauwels, Mark Huiskes, Ben Schouten
For meaningful searching and browsing in image and video collections, accurate representations of image content
in terms of a wide range of features are crucial. Automatic generation of features shows great promise, but perfection
yet remains a distant dream. Taking the results of automatic methods as a starting point, we are designing interfaces for
both interactive feature annotation and interactive feature design.
Feature annotation is aimed at conveniently enhancing the automatically obtained features using for instance
automatic speech recognition technology and drag-and-drop interfaces.
Interactive feature design uses various mining and learning approaches as a means to explore feature spaces
for adaptive feature creation.
Numerical Methods for Image Processing
Paul de Zeeuw
An image processing method is devised which involves the concepts of
image transforms, partial differential equations and multiresolution
all in one. The resulting method has implications for image fusion,
segmentation and denoising.
Computer-Aided Photo-Identification of Cetaceans
Eric Pauwels, Elena Ranguelova, Adri Steenbeek
Individual identification of animals is an important issue in marine biology and zoology in general. The method of photo-identification relies on the uniqueness of the physical characteristics of each individual and is a tedious task given the large size of the photographic catalogues. The aim is to develop algorithms for segmentation of anatomical structures, extraction of shape features and design of feature matching algorithms invariant under illumination changes and geometric distortions. Semi-automatic segmentation method based on watershed transformation combined with regions of interest extraction and feature description are being proposed for photo-identification of humpback whales.
More information...
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