1998
@inproceedings{FDS1998,
vgclass = {refpap},
vgproject = {cbir},
author = {Pascal Faudemay and Gwena\"{e}l Durand and Claude Seyrat
and Nicolas Tondre},
title = {Indexing and retrieval of multimedia objects at different
levels of granularity},
editor = {C.-C. Jay Kuo and Shih-Fu Chang and Sethuraman
Panchanathan},
booktitle = {Multimedia Storage and Archiving Systems III (VV02)},
address = {Boston, Massachusetts, USA},
volume = {3527},
series = {SPIE Proceedings},
pages = {112--121},
month = {November},
year = {1998},
note = {(SPIE Symposium on Voice, Video and Data Communications)},
abstract = {Intelligent access to multimedia databases for ``naive
user'' should probably be based on queries formulation by ``intelligent
agents''. These agents should ``understand'' the semantics of the
source contents, learn user preferences and deliver to the user a
subset of source contents, for further navigation. The goal of such
systems should be to enable ``zero-command'' access to the contents,
while keeping the freedom of choice of the user. Such systems should
interpret multimedia contents in terms of multiple audiovisual objects
(from video to visual or audio object), and on actions and scenarios.
In our project we have developed a method for image segmentation into
semantic objects, even in the case of still images. We use this method,
and user-defined collections of such objects, to facilitate temporal
segmentation of videos into multiple semantic granules from story and
sequence to object, and to characterize stories contents. For this
purpose, we also use audio information from selected parts of the
video. Stories are characterized by a set of visual concepts and words,
and semantic similarity between stories is evaluated based on
information retrieval methods. The system learns user preferences, and
incrementally builds a user profile, which is used to present relevant
stories in an appropriate order. This approach was used to build a
mockup of a simple ``push'' engine, which is presently being
experimented.},
}