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[BFD11c] Indexation of large satellite image repositories using small training sets
Conférence Internationale avec comité de lecture : Image Information Mining: Geospatial Intelligence from Earth Observation (ESA-EUSC-JRC 2011), March 2011, Ispra, Italy,Mots clés: statistical learning, Support Vector Machines, Earth Observation Mining, Image Databases, Relevance Feedback
Résumé:
The increasing number and resolution of earth observation
(EO) imaging sensors has had a significant impact on both
the acquired image data volume and the information content
in images. There is consequently a strong need for highly
efficient search tools for EO image databases and for search
methods to automatically identify and recognize structures
within EO images. Content Based Image Retrieval (CBIR)
and automatic image annotation systems have been designed
to tackle the problem of image retrieval in large image databases.
These two systems achieve a common goal which
is to learn the mapping function between low-level visual
features and high-level image semantics. In this paper, we
present an overview of two approaches that address the problem
of learning this mapping function from a few training
examples in the case of auto-annotation and CBIR systems
respectively.
Equipe:
vertigo
Collaboration:
DLR
BibTeX
| @inproceedings { | |||
| BFD11c, | |||
| title | = | "{Indexation of large satellite image repositories using small training sets}", | |
| author | = | " P. Blanchart and M. Ferecatu and M. Datcu ", | |
| booktitle | = | "{Image Information Mining: Geospatial Intelligence from Earth Observation (ESA-EUSC-JRC 2011)}", | |
| year | = | 2011, | |
| month | = | "March", | |
| address | = | "Ispra, Italy", | |
| } | |||
