Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

Multilingual search for cultural heritage archives via combining multiple translation resources

The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system

Recent development in XML-IR

The Web is characterized by a huge amount of heterogeneous data sources, which have different media support and format representation. Because XML can represent files of different formats, it can play an important role in IR since it is becoming a standard form for data representation and exchange over the Web. Under this assumption, the problem of querying heterogeneous sources can be reduced to the problem of querying XML data sources. This paper shows the influence of XML on the IR techniques and methodologies during the last five years through serving over 400 papers published in different conferences and journals

Language-based multimedia information retrieval

This paper describes various methods and approaches for language-based multimedia information retrieval, which have been developed in the projects POP-EYE and OLIVE and which will be developed further in the MUMIS project. All of these project aim at supporting automated indexing of video material by use of human language technologies. Thus, in contrast to image or sound-based retrieval methods, where both the query language and the indexing methods build on non-linguistic data, these methods attempt to exploit advanced text retrieval technologies for the retrieval of non-textual material. While POP-EYE was building on subtitles or captions as the prime language key for disclosing video fragments, OLIVE is making use of speech recognition to automatically derive transcriptions of the sound tracks, generating time-coded linguistic elements which then serve as the basis for text-based retrieval functionality

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents

The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications

Integrated content presentation for multilingual and multimedia information access

For multilingual and multimedia information retrieval from multiple potentially distributed collections generating the output in the form of standard ranked lists may often mean that a user has to explore the contents of many lists before finding sufficient relevant or linguistically accessible material to satisfy their information need. In some situations delivering an integrated multilingual multimedia presentation could enable the user to explore a topic allowing them to select from among a range of available content based on suitably chosen displayed metadata. A presentation of this type has similarities with the outputs of existing adaptive hypermedia systems. However, such systems are generated based on “closed” content with sophisticated user and domain models. Extending them to “open” domain information retrieval applications would raise many issues. We present an outline exploration of what will form a challenging new direction for research in multilingual information access

Disaster Monitoring with Wikipedia and Online Social Networking Sites: Structured Data and Linked Data Fragments to the Rescue?

In this paper, we present the first results of our ongoing early-stage research on a realtime disaster detection and monitoring tool. Based on Wikipedia, it is language-agnostic and leverages user-generated multimedia content shared on online social networking sites to help disaster responders prioritize their efforts. We make the tool and its source code publicly available as we make progress on it. Furthermore, we strive to publish detected disasters and accompanying multimedia content following the Linked Data principles to facilitate its wide consumption, redistribution, and evaluation of its usefulness.Comment: Accepted for publication at the AAAI Spring Symposium 2015: Structured Data for Humanitarian Technologies: Perfect fit or Overkill? #SD4HumTech1

Multimedia retrieval in MultiMatch: The impact of speech transcript errors on search behaviour

This study discusses the findings of an evaluation study on the performance of a multimedia multimodal information access sub-system (MIAS), incorporating automatic speech recognition technology (ASR) to automatically transcribe the speech content of video soundtracks. The study’s results indicate that an information-rich but minimalist graphical interface is preferred. It was also discovered that users tend to have a misplaced confidence in the accuracy of ASR-generated speech transcripts, thus they are not inclined to conduct a systematic auditory inspection (their usual search behaviour) of a video’s soundtrack if the query term does not appear in the transcript. In order to alert the user to the possibility that a search term may be incorrectly recognised as some other word, a matching algorithm is proposed that searches for word sequences of similar phonemic structure to the query term