Exploiting term co-occurrence for enhancing automated image annotation

Ainhoa Llorente; Simon Overell; Haiming Liu; Rui Hu; Adam Rae; Jianhan Zhu; Dawei Song; Stefan Rüger

doi:10.1007/978-3-642-04447-2_79

Exploiting term co-occurrence for enhancing automated image annotation

Ainhoa Llorente^*, Simon Overell, Haiming Liu, Rui Hu, Adam Rae, Jianhan Zhu, Dawei Song, Stefan Rüger

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.

Original language	English
Title of host publication	Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers
Pages	632-639
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-04447-2_79
Publication status	Published - 2009
Externally published	Yes
Event	9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008 - Aarhus, Denmark Duration: 17 Sept 2008 → 19 Sept 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5706 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008
Country/Territory	Denmark
City	Aarhus
Period	17/09/08 → 19/09/08

Keywords

Automated image annotation
Image analysis
Semantic similarity
Statistical co-occurrence

Access to Document

10.1007/978-3-642-04447-2_79

Cite this

Llorente, A., Overell, S., Liu, H., Hu, R., Rae, A., Zhu, J., Song, D., & Rüger, S. (2009). Exploiting term co-occurrence for enhancing automated image annotation. In Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers (pp. 632-639). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5706 LNCS). https://doi.org/10.1007/978-3-642-04447-2_79

Llorente, Ainhoa ; Overell, Simon ; Liu, Haiming et al. / Exploiting term co-occurrence for enhancing automated image annotation. Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers. 2009. pp. 632-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.",

keywords = "Automated image annotation, Image analysis, Semantic similarity, Statistical co-occurrence",

author = "Ainhoa Llorente and Simon Overell and Haiming Liu and Rui Hu and Adam Rae and Jianhan Zhu and Dawei Song and Stefan R{\"u}ger",

year = "2009",

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booktitle = "Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers",

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Llorente, A, Overell, S, Liu, H, Hu, R, Rae, A, Zhu, J, Song, D & Rüger, S 2009, Exploiting term co-occurrence for enhancing automated image annotation. in Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5706 LNCS, pp. 632-639, 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, 17/09/08. https://doi.org/10.1007/978-3-642-04447-2_79

Exploiting term co-occurrence for enhancing automated image annotation. / Llorente, Ainhoa; Overell, Simon; Liu, Haiming et al.
Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers. 2009. p. 632-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5706 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Exploiting term co-occurrence for enhancing automated image annotation

AU - Llorente, Ainhoa

AU - Overell, Simon

AU - Liu, Haiming

AU - Hu, Rui

AU - Rae, Adam

AU - Zhu, Jianhan

AU - Song, Dawei

AU - Rüger, Stefan

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AB - This paper describes an application of statistical co-occurrence techniques that built on top of a probabilistic image annotation framework is able to increase the precision of an image annotation system. We observe that probabilistic image analysis by itself is not enough to describe the rich semantics of an image. Our hypothesis is that more accurate annotations can be produced by introducing additional knowledge in the form of statistical co-occurrence of terms. This is provided by the context of images that otherwise independent keyword generation would miss. We applied our algorithm to the dataset provided by ImageCLEF 2008 for the Visual Concept Detection Task (VCDT). Our algorithm not only obtained better results but also it appeared in the top quartile of all methods submitted in ImageCLEF 2008.

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Llorente A, Overell S, Liu H, Hu R, Rae A, Zhu J et al. Exploiting term co-occurrence for enhancing automated image annotation. In Evaluating Systems for Multilingual and Multimodal Information Access - 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Revised Selected Papers. 2009. p. 632-639. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04447-2_79

Exploiting term co-occurrence for enhancing automated image annotation

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