ANR PHOENIX
Workgroup SYMBOLETS
[SYMBOlic waveLETS in data mining]
Context
and ‘SYMBOLET’ framework
Information discovery
from huge numeric
image datasets usually relies on data mining techniques. The steps involved in
a data mining technique can be summarized as follows:
(i)
extraction of local
and spatial features, generally consisting in symbols and
(ii)
mining (usually
exhaustive) for the retrieval of frequent/occurrent and connected symbol evolutions.
The latter step (ii) is
sensitive to the reliability of the description features extracted in step (i).
In this respect, many strategies have been investigated for pre-processing data
(image filtering techniques, oriented pyramid structuring, etc.) so as to increase feature reliability. These strategies
mainly apply in the so-called numeric domain (digital image concern) where
arithmetic sum and product operations imply nice algebraic properties.
The ‘SYMBOLET’
framework is a different approach: it consists in defining functional representations
for symbolic data (feature domain). Regularity/occurrences of symbols will be
embedded in a symbolic algebra framework that involves re-defining standard
addition and difference operations.
Team