Standard
Semantic image retrieval using region-based relevance feedback. / Torres, José Manuel
; Hutchison, David; Reis, Luís Paulo.
Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers. ed. / Stéphane Marchand-Maillet; Eric Bruno; Andreas Nürnberger; Marcin Detyniecki. Berlin: Springer, 2007. p. 192-206 (Lecture Notes in Computer Science; Vol. 4398).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Torres, JM
, Hutchison, D & Reis, LP 2007,
Semantic image retrieval using region-based relevance feedback. in S Marchand-Maillet, E Bruno, A Nürnberger & M Detyniecki (eds),
Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers. Lecture Notes in Computer Science, vol. 4398, Springer, Berlin, pp. 192-206.
https://doi.org/10.1007/978-3-540-71545-0_15APA
Torres, J. M.
, Hutchison, D., & Reis, L. P. (2007).
Semantic image retrieval using region-based relevance feedback. In S. Marchand-Maillet, E. Bruno, A. Nürnberger, & M. Detyniecki (Eds.),
Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers (pp. 192-206). (Lecture Notes in Computer Science; Vol. 4398). Springer.
https://doi.org/10.1007/978-3-540-71545-0_15Vancouver
Torres JM
, Hutchison D, Reis LP.
Semantic image retrieval using region-based relevance feedback. In Marchand-Maillet S, Bruno E, Nürnberger A, Detyniecki M, editors, Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers. Berlin: Springer. 2007. p. 192-206. (Lecture Notes in Computer Science). doi: 10.1007/978-3-540-71545-0_15
Author
Torres, José Manuel
; Hutchison, David ; Reis, Luís Paulo. /
Semantic image retrieval using region-based relevance feedback. Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers. editor / Stéphane Marchand-Maillet ; Eric Bruno ; Andreas Nürnberger ; Marcin Detyniecki. Berlin : Springer, 2007. pp. 192-206 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{526b22e46d43481a8b2b45701903ee52,
title = "Semantic image retrieval using region-based relevance feedback",
abstract = "A structured vocabulary of terms, such as a textual thesaurus, provides a way to conceptually describe visual information. The retrieval model described in this paper combines a conceptual and a visual layer as a first step towards the integration of ontologies and content-based image retrieval. Terms are related with image regions through a weighted association. This model allows the execution of concept-level queries, fulfilling user expectations and reducing the so-called semantic gap. Region-based relevance feedback is used to improve the quality of results in each query session and to help in the discovery of associations between text and image. The learning mechanism, whose function is to discover existing term-region associations, is based on a clustering algorithm applied over the features space and on propagation functions, which acts in each cluster where new information is available from user interaction. This approach is validated with the presentation of promising results obtained using the VOIR - Visual Object Information Retrieval system.",
author = "Torres, {Jos{\'e} Manuel} and David Hutchison and Reis, {Lu{\'i}s Paulo}",
year = "2007",
doi = "10.1007/978-3-540-71545-0_15",
language = "English",
isbn = "978-3-540-71544-3",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "192--206",
editor = "Marchand-Maillet, {St{\'e}phane } and Bruno, {Eric } and Andreas N{\"u}rnberger and Detyniecki, {Marcin }",
booktitle = "Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers",
}
RIS
TY - GEN
T1 - Semantic image retrieval using region-based relevance feedback
AU - Torres, José Manuel
AU - Hutchison, David
AU - Reis, Luís Paulo
PY - 2007
Y1 - 2007
N2 - A structured vocabulary of terms, such as a textual thesaurus, provides a way to conceptually describe visual information. The retrieval model described in this paper combines a conceptual and a visual layer as a first step towards the integration of ontologies and content-based image retrieval. Terms are related with image regions through a weighted association. This model allows the execution of concept-level queries, fulfilling user expectations and reducing the so-called semantic gap. Region-based relevance feedback is used to improve the quality of results in each query session and to help in the discovery of associations between text and image. The learning mechanism, whose function is to discover existing term-region associations, is based on a clustering algorithm applied over the features space and on propagation functions, which acts in each cluster where new information is available from user interaction. This approach is validated with the presentation of promising results obtained using the VOIR - Visual Object Information Retrieval system.
AB - A structured vocabulary of terms, such as a textual thesaurus, provides a way to conceptually describe visual information. The retrieval model described in this paper combines a conceptual and a visual layer as a first step towards the integration of ontologies and content-based image retrieval. Terms are related with image regions through a weighted association. This model allows the execution of concept-level queries, fulfilling user expectations and reducing the so-called semantic gap. Region-based relevance feedback is used to improve the quality of results in each query session and to help in the discovery of associations between text and image. The learning mechanism, whose function is to discover existing term-region associations, is based on a clustering algorithm applied over the features space and on propagation functions, which acts in each cluster where new information is available from user interaction. This approach is validated with the presentation of promising results obtained using the VOIR - Visual Object Information Retrieval system.
U2 - 10.1007/978-3-540-71545-0_15
DO - 10.1007/978-3-540-71545-0_15
M3 - Conference contribution/Paper
SN - 978-3-540-71544-3
T3 - Lecture Notes in Computer Science
SP - 192
EP - 206
BT - Adaptive Multimedia Retrieval: User, Context, and Feedback 4th International Workshop, AMR 2006, Geneva, Switzerland, July 27-28, 2006, Revised Selected Papers
A2 - Marchand-Maillet, Stéphane
A2 - Bruno, Eric
A2 - Nürnberger, Andreas
A2 - Detyniecki, Marcin
PB - Springer
CY - Berlin
ER -
