Octopus™ is an aggressive mechanism for
user-adaptive search of multi-modal information based on multifaceted
knowledge base, an ongoing research project at Dept. of Computer
Engineering and Information Technology of City University of Hong Kong.
Octopus™
aims to promote an innovative scenario of multimedia retrieval --
users retrieve semantically meaningful and user-adapted multi-modal objects
(including text documents, images, video clips, audio clips) through
easy-to-compose queries -- which brings about brand-new user experiences in
searching the Web, digital libraries, or other multimedia repositories.
To achieve
the above objective, we have designed some unique
features for Octopus™, including a multifaceted
knowledge base, link analysis based retrieval algorithms. and
a learning-from-interactions strategy. An prototypical system is
under implementation, the online demonstration
of which will be available soon. The published papers
about (or related to) Octopus™ can be downloaded from this
site.
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The proliferation of multimedia
data in a variety of modalities challenges the traditional retrieval
technologies in the following aspects:
- Users prefer multi-modal
information (medias of various types) to a single type of media.
- Users prefer semantically
meaningful results.
- Each individual user has
particular need that differs from each other.
The
three requirements go beyond the scope of conservative traditional
retrieval technologies, which focus on single types of media using
low-level data features. In contrast, Octopus™ has an rather
aggressive spirit in that it deal with multi-modal objects by exploring
a broad range of knowledge as well as user interactions. Therefore, Octopus™ can meet all the
requirements of multimedia retrieval in a variety of environments. |
What's Octopus