Annotated collections of images and videos are a necessary basis for the successful development of multimedia retrieval systems. The underlying models of such systems rely heavily on quality and availability of large training collections. The annotation of large collections, however, is a time-consuming and error prone task as it has to be performed by human annotators. In this paper we present the IBM Efficient Video Annotation (EVA) system, a server-based tool for semantic concept annotation of large video and image collections. It is optimised for collaborative annotation and includes features such as workload sharing and support in conducting inter-annotator analysis. We discuss initial results of an ongoing user-evaluation of this system. The results are based on data collected during the 2005 TRECVID Annotation Forum, where more than 100 annotators have been using the system.

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Text-based search using video speech transcripts is a popular approach for granular video retrieval at the shot or story level. However, misalignment of speech and visual tracks, speech transcription errors, and other characteristics of video content pose unique challenges for this video retrieval approach.In this paper, we explore several automatic query refinement methods to address these issues. We consider two query expansion methods based on pseudo-relevance feedback and one query refinement method based on semantic text annotation. We evaluate these approaches in the context of the TRECVID 2005 Video Retrieval Benchmark using a baseline approach without any refinement. To improve robustness, we also consider a query-independent fusion approach. We show that this combined approach can outperform the baseline for most query topics, with improvements of up to 40%. We also show that query-dependent fusion approaches can potentially improve the results further, leading to 18-75% gains when tuned with optimal fusion parameters.