Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

The TREC2001 video track: information retrieval on digital video information

The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable

A Probabilistic Multimedia Retrieval Model and its Evaluation

We present a probabilistic model for the retrieval of multimodal documents. The model is based on Bayesian decision theory and combines models for text-based search with models for visual search. The textual model is based on the language modelling approach to text retrieval, and the visual information is modelled as a mixture of Gaussian densities. Both models have proved successful on various standard retrieval tasks. We evaluate the multimodal model on the search task of TREC′s video track. We found that the disclosure of video material based on visual information only is still too difficult. Even with purely visual information needs, text-based retrieval still outperforms visual approaches. The probabilistic model is useful for text, visual, and multimedia retrieval. Unfortunately, simplifying assumptions that reduce its computational complexity degrade retrieval effectiveness. Regarding the question whether the model can effectively combine information from different modalities, we conclude that whenever both modalities yield reasonable scores, a combined run outperforms the individual runs

PFTijah: text search in an XML database system

This paper introduces the PFTijah system, a text search system that is integrated with an XML/XQuery database management system. We present examples of its use, we explain some of the system internals, and discuss plans for future work. PFTijah is part of the open source release of MonetDB/XQuery

Overview of the TREC 2013 Federated Web Search Track

The TREC Federated Web Search track is intended to promote research related to federated search in a realistic web setting, and hereto provides a large data collection gathered from a series of online search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in federated search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising research directions and an outlook on the 2014 edition of the track are provided as well

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