Abstract
In this paper we present details on the joint submission of TU Berlin and Fraunhofer FIRST to the ImageCLEF 2011 Photo Annotation Task.We sought to experiment with extensions of Bag-of-Words (BoW) models at several levels and to apply several kernel-based learning methods recently developed in our group. For classifier training we used non-sparse multiple kernel learning (MKL) and an efficient multi-task learning (MTL) heuristic based on MKL over kernels from classifier outputs. For the multi-modal fusion we used a smoothing method on tag-based features inspired by Bag-of-Words soft mappings and Markov random walks. We submitted one multi-modal run extended by the user tags and four purely visual runs based on Bag-of-Words models. Our best visual result which used the MTL method was ranked first according to mean average precision (MAP) within the purely visual submissions. Our multi-modal submission achieved the first rank by MAP among the multi-modal submissions and the best MAP among all submissions. Submissions by other groups such as BPACAD, CAEN, UvA-ISIS, LIRIS were ranked closely.
| Original language | English |
|---|---|
| Title of host publication | CEUR Workshop Proceedings |
| Publisher | CEUR-WS |
| Volume | 1177 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 2011 Working Notes for CLEF Conference, CLEF 2011 - Amsterdam, Netherlands Duration: 2011 Sep 19 → 2011 Sep 22 |
Other
| Other | 2011 Working Notes for CLEF Conference, CLEF 2011 |
|---|---|
| Country | Netherlands |
| City | Amsterdam |
| Period | 11/9/19 → 11/9/22 |
Fingerprint
Keywords
- Bag-of-words
- Image classification
- Image clef
- Multi-task learning
- Multiple kernel learning
- Photo annotation
- Theseus
ASJC Scopus subject areas
- Computer Science(all)
Cite this
The joint submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the image CLEF 2011 photo annotation task. / Binder, Alexander; Samek, Wojciech; Kloft, Marius; Müller, Christina; Muller, Klaus; Kawanabe, Motoaki.
CEUR Workshop Proceedings. Vol. 1177 CEUR-WS, 2011.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - The joint submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the image CLEF 2011 photo annotation task
AU - Binder, Alexander
AU - Samek, Wojciech
AU - Kloft, Marius
AU - Müller, Christina
AU - Muller, Klaus
AU - Kawanabe, Motoaki
PY - 2011
Y1 - 2011
N2 - In this paper we present details on the joint submission of TU Berlin and Fraunhofer FIRST to the ImageCLEF 2011 Photo Annotation Task.We sought to experiment with extensions of Bag-of-Words (BoW) models at several levels and to apply several kernel-based learning methods recently developed in our group. For classifier training we used non-sparse multiple kernel learning (MKL) and an efficient multi-task learning (MTL) heuristic based on MKL over kernels from classifier outputs. For the multi-modal fusion we used a smoothing method on tag-based features inspired by Bag-of-Words soft mappings and Markov random walks. We submitted one multi-modal run extended by the user tags and four purely visual runs based on Bag-of-Words models. Our best visual result which used the MTL method was ranked first according to mean average precision (MAP) within the purely visual submissions. Our multi-modal submission achieved the first rank by MAP among the multi-modal submissions and the best MAP among all submissions. Submissions by other groups such as BPACAD, CAEN, UvA-ISIS, LIRIS were ranked closely.
AB - In this paper we present details on the joint submission of TU Berlin and Fraunhofer FIRST to the ImageCLEF 2011 Photo Annotation Task.We sought to experiment with extensions of Bag-of-Words (BoW) models at several levels and to apply several kernel-based learning methods recently developed in our group. For classifier training we used non-sparse multiple kernel learning (MKL) and an efficient multi-task learning (MTL) heuristic based on MKL over kernels from classifier outputs. For the multi-modal fusion we used a smoothing method on tag-based features inspired by Bag-of-Words soft mappings and Markov random walks. We submitted one multi-modal run extended by the user tags and four purely visual runs based on Bag-of-Words models. Our best visual result which used the MTL method was ranked first according to mean average precision (MAP) within the purely visual submissions. Our multi-modal submission achieved the first rank by MAP among the multi-modal submissions and the best MAP among all submissions. Submissions by other groups such as BPACAD, CAEN, UvA-ISIS, LIRIS were ranked closely.
KW - Bag-of-words
KW - Image classification
KW - Image clef
KW - Multi-task learning
KW - Multiple kernel learning
KW - Photo annotation
KW - Theseus
UR - http://www.scopus.com/inward/record.url?scp=84922032448&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922032448&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84922032448
VL - 1177
BT - CEUR Workshop Proceedings
PB - CEUR-WS
ER -