RT Conference Proceedings
T1 Improving tag transfer for image annotation using visual and semantic information
A1 Rodriguez-Vaamonde, Sergio
A1 Torresani, Lorenzo
A1 Espinosa, Koldo
A1 Garrote, Estibaliz
AB This paper addresses the problem of image annotation using a combination of visual and semantic information. Our model involves two stages: a Nearest Neighbor computation and a tag transfer stage that collects the final annotations. For the latter stage, several algorithms have been implemented in the past using labels' information or including implicitly some visual features. In this paper we propose a novel algorithm for tag transfer that takes advantage explicitly of semantic and visual information. We also present a structured training procedure based on a concept we have called Image Networking: all the images in a training database are 'connected' visually and semantically, so it is possible to exploit these connections to learn the tag transfer parameters at annotation time. This learning is local for the test image and it exploits the information obtained in the Nearest Neighbor computation stage. We demonstrate that our approach achieves state-of-The-art performance on the ImageCLEF2011 dataset.
PB IEEE Computer Society
SN 9781479939909
SN 1949-3991
YR 2014
FD 2014
LK https://hdl.handle.net/11556/1534
UL https://hdl.handle.net/11556/1534
LA eng
NO Rodriguez-Vaamonde , S , Torresani , L , Espinosa , K & Garrote , E 2014 , Improving tag transfer for image annotation using visual and semantic information . in 2014 12th International Workshop on Content-Based Multimedia Indexing, CBMI 2014 . , 6849846 , Proceedings - International Workshop on Content-Based Multimedia Indexing , IEEE Computer Society , 12th International Workshop on Content-Based Multimedia Indexing, CBMI 2014 , Klagenfurt , Austria , 18/06/14 . https://doi.org/10.1109/CBMI.2014.6849846
NO conference
DS TECNALIA Publications
RD 1 sept 2024