Evaluating a probabilistic model for cross-lingual information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Statistical machine translation by parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Fast image auto-annotation with discretized feature distance measures
Machine Graphics & Vision International Journal
Effective content-based video retrieval using pattern-indexing and matching techniques
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In this study, we present a systematic evaluation of machine translation methods applied to the image annotation problem. We used the well-studied Corel data set and the broadcast news videos used by TRECVID 2003 as our dataset. We experimented with different models of machine translation with different parameters. The results showed that the simplest model produces the best performance. Based on this experience, we also proposed a new method, based on cross-lingual information retrieval techniques, and obtained a better retrieval performance.