Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ainhoa Llorente"'
Publikováno v:
CIVR
In this paper, we explore different ways of formulating new evaluation measures for multi-label image classification when the vocabulary of the collection adopts the hierarchical structure of an ontology. We apply several semantic relatedness measure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b154e7a1ac8c3b856a2f61ac0146cdc6
https://doi.org/10.1145/1816041.1816086
https://doi.org/10.1145/1816041.1816086
Publikováno v:
ImageCLEF ISBN: 9783642151804
ImageCLEF
ImageCLEF
This chapter presents an academic and research perspective on the impact and importance of ImageCLEF and similar evaluation workshops in multimedia information retrieval (MIR). Three main themes are examined: the position of ImageCLEF compared with o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0490bfb2179b92ec7346442df2afdda9
https://doi.org/10.1007/978-3-642-15181-1_27
https://doi.org/10.1007/978-3-642-15181-1_27
Publikováno v:
Proceedings of the ACM International Conference on Image and Video Retrieval-CIVR '10
CIVR
CIVR
In this paper, we propose a direct image retrieval framework based on Markov Random Fields (MRFs) that exploits the semantic context dependencies of the image. The novelty of our approach lies in the use of different kernels in our non-parametric den
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faee3fd88d37fc7a3e177b180938f789
http://oro.open.ac.uk/23503/1/p243-llorente.pdf
http://oro.open.ac.uk/23503/1/p243-llorente.pdf
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642157509
CLEF (2)
CLEF (2)
The goal of this research is to explore several semantic relatedness measures that help to refine annotations generated by a base-line non-parametric density estimation algorithm. Thus, we analyse the benefits of performing a statistical correlation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bb6c5a9c6f333183cfe2822b7e59413
https://doi.org/10.1007/978-3-642-15751-6_40
https://doi.org/10.1007/978-3-642-15751-6_40
Publikováno v:
Semantic Multimedia ISBN: 9783642105425
SAMT
SAMT
This paper describes a novel approach that automatically refines the image annotations generated by a non-parametric density estimation model. We re-rank these initial annotations following a heuristic algorithm, which uses semantic relatedness measu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33d25b9eecc0fb4c06b7626c476f0682
https://doi.org/10.1007/978-3-642-10543-2_22
https://doi.org/10.1007/978-3-642-10543-2_22
Autor:
Stefan Rüger, Ainhoa Llorente
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642009570
ECIR
ECIR
We examine whether a traditional automated annotation system can be improved by using background knowledge. Traditional means any machine learning approach together with image analysis techniques. We use as a baseline for our experiments the work don
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::132deede2145dcf9320082e9bbdede9a
https://doi.org/10.1007/978-3-642-00958-7_52
https://doi.org/10.1007/978-3-642-00958-7_52
Autor:
Simon Overell, Dawei Song, Adam Rae, Stefan Rüger, Rui Hu, Ainhoa Llorente, Haiming Liu, Jianhan Zhu
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642044465
CLEF
CLEF
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3ad1cf480424bc3bccc29af66055fde1
https://doi.org/10.1007/978-3-642-04447-2_79
https://doi.org/10.1007/978-3-642-04447-2_79