Systematic evaluation of machine translation methods for image and video annotation

Abstract

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.