RECVID as a Re-Usable Test-Collection for Video Retrieval

TRECVID has been running as a video retrieval benchmarking platform for a number of years now. Some progress seems to be made in the area of video retrieval, but also it has been shown that many of the differences in scores between tested approaches are nonsignificant. This paper studies the reliability of the TRECVID search collections for measuring video retrieval effectiveness and investigates how useful the collections are for re-use

Experimental evaluation of a generative probabilistic image retrieval model on 'easy' data

We present evaluation results of a generative probabilistic image retrieval model using `easy data'. Previous research into our model's retrieval effectiveness has used the test collection developed at TREC's Video Track, but as discussed in detail in [WeVr:SIGIR:03], its search task has been too difficult to measure actual performance of the retrieval model. The `easy data' experiments presented here evaluate our model under varying model parameters on the Corel set. The Corel data set is relatively easy because images are nicely grouped into coherent themes, the within theme similarity is high and the across theme similarity relatively low. These properties make Corel a nice vehicle for testing, presenting or selling new content based retrieval techniques and models. In contrast to the TREC data, the Corel collection gives statistically significant differences between varying experimental conditions, so we get more insight in the model's behaviour. We then discuss at length the limitations of the results obtained using this data set, comparing the experiments performed here to those on the TREC data

Multimedia retrieval using multiple examples

The paper presents a variant of our generative probabilistic multimedia retrieval model that is suitable for information needs expressed as multiple examples. Results have been evaluated on the TRECVID 2003 collection

Surface features in video retrieval

This paper assesses the usefulness of surface features in a multimedia retrieval setting. Surface features describe the metadata or structure of a document rather than the content. We note that the distribution of these features varies across topics. The paper shows how these distributions can be obtained through relevance feedback and how this allows for adaptation of (content-based) search results for topic or user preference. An analysis of the distribution of surface features in the TRECVID collection indicates that they are potentially useful, and a preliminary feedback experiment confirms that exploiting surface features can improve retrieval effectiveness

Structural features in content oriented XML retrieval

The structural features of XML components are an extra source of information that should be used in a content-oriented retrieval task on this type of documents. In this paper we explore one of the structural features from the INEX collection that could be used in content-oriented search. We analyse the gain this knowledge could add to the performance of an information retrieval system and present a first approach on how this structural information could be extracted from a relevance feedback process to be used as priors in a language modelling framework