A Weakly Supervised Approach for Semantic Image Indexing and Retrieval

LES AUTEURS Nicolas Maillot and Monique Thonnat

RESUME:

This paper presents a new approach for building semantic image indexing and retrieval systems.
Our approach is composed of four phases : (1) knowledge acquisition, (2) weakly-supervised learning, (3) indexing and (4) retrieval.
Phase 1 is driven by a visual concept ontology which helps the expert to define low-level features useful to characterize object classes.
Phase 2 uses acquired knowledge and image samples to learn the mapping between image data and visual concepts.
Image indexing phase (phase 3) is fully automatic and produces semantic annotations of the images to index.
The symbolic nature of querying enables user-friendly and fast retrieval (phase 4).
We have applied our approach to the domain of transport vehicles (i.e. motorbikes, aircrafts, cars).

Mots clé: Semantic-based retrieval, Learning in retrieval, Content analysis and understanding

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BibTeX reference:

@INPROCEEDINGS{Maillot05,
author = {Nicolas Maillot and Monique Thonnat},
title = {A Weakly Supervised Approach for Semantic Image Indexing and 
Retrieval.},
booktitle = {CIVR},
title = {Image and Video Retrieval, 4th International Conference, CIVR 
2005, Singapore, July 20-22, 2005, Proceedings},
year = {2005},
editor = {Wee Kheng Leow and Michael S. Lew and Tat-Seng Chua and Wei-Ying
Ma and Lekha Chaisorn and Erwin M. Bakker},
volume = {3568},
series = {Lecture Notes in Computer Science},
pages = {629-638},
publisher = {Springer},
isbn = {3-540-27858-3}
}

Dernière mise à jour : 8/11/05
Catherine.Martin@sophia.inria.fr