The Mediamill Semantic Video Search Engine

Worring, Marcel; Snoek, Cees G. M.; Rooij, O. de; Nguyen, G.P.; Smeulders, A.W.M.

doi:10.1109/icassp.2007.367294

Cited by 34 publications

(24 citation statements)

References 7 publications

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“…To give an idea of the complexity and structure of such a system, Figure 2 depicts the envisioned architecture of the MediaMill system. For an exhaustive description of the architecture, we refer to Worring et al (2007). Here, we give a summary of the most relevant components for our purposes in order to demonstrate how MuNCH's contributions Þ t within the bigger picture of a working video processing and retrieval system.…”

Section: Components Of a Video Retrieval Systemmentioning

confidence: 99%

See 1 more Smart Citation

A Multidisciplinary Approach to Unlocking Television Broadcast Archives

Hollink¹,

Schreiber²,

Huurnink³

et al. 2009

Interdisciplinary Science Reviews

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Audiovisual material is a vital component of the world's heritage but it remains diffi cult to access. With the Netherlands Institute for Sound and Vision as one of its partners, the MuNCH project aims to investigate new methods for improving access to a wide range of audiovisual documents. MuNCH brings together three research fi elds: multimedia analysis, language technology and semantic technologies.Within the MuNCH project, we have investigated several combinations of these fi elds. We have compared text matching, ontology querying, and semantic visual querying as methods to translate a multimedia query to the vocabulary of the retrieval system. In addition, we have investigated how users make such a translation, and have used this as a benchmark to create automatic methods. We have used multimedia technology to automatically detect objects and scenes as they occur in video, and made use of language technology to exploit automatic transcriptions of speech. We have enriched the Sound and Vision thesaurus that is used to annotate the TV programmes in order to provide a user with a wider range of search results.In order to verify the results of the project against real user needs, MuNCH has participated in the creation of a logging system which monitors the usage of the Sound and Vision catalogue system. Insights in the needs of real users will be used as input for all three of MuNCH's research strands.keywords Multimedia, Retrieval, Content-based image retrieval, Semantic web, Language technology, User queries 254 LAURA HOLLINK et al.

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Section: Components Of a Video Retrieval Systemmentioning

confidence: 99%

“…The MediaMill semantic video search engine (Worring et al 2007) is such an integrated system. Many of MuNCH's contributions are -or will beimplemented as components in the MediaMill system.…”

Section: Components Of a Video Retrieval Systemmentioning

confidence: 99%

A Multidisciplinary Approach to Unlocking Television Broadcast Archives

Hollink¹,

Schreiber²,

Huurnink³

et al. 2009

Interdisciplinary Science Reviews

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“…Previous work mapping text to concepts relies upon exact or approximate string matching or by associating ASR transcripts [10] with the concepts. Snoek et al [18] use the vector space model to match text queries to concept descriptions which are used for identifying relevant videos. Neo et al [11] perform expansion of both the text query and the concept description using Wordnet and a sample of external news.…”

Section: Related Workmentioning

confidence: 99%

Zero-shot video retrieval using content and concepts

Dalton

Allan

Mirajkar

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Recent research in video retrieval has been successful at finding videos when the query consists of tens or hundreds of sample relevant videos for training supervised models. Instead, we investigate unsupervised zero-shot retrieval where no training videos are provided: a query consists only of a text statement. For retrieval, we use text extracted from images in the videos, text recognized in the speech of its audio track, as well as automatically detected semantically meaningful visual video concepts identified with widely varying confidence in the videos. In this work we introduce a new method for automatically identifying relevant concepts given a text query using the Markov Random Field (MRF) retrieval framework. We use source expansion to build rich textual representations of semantic video concepts from large external sources such as the web. We find that concept-based retrieval significantly outperforms text based approaches in recall. Using an evaluation derived from the TRECVID MED'11 track, we present early results that an approach using multi-modal fusion can compensate for inadequacies in each modality, resulting in substantial effectiveness gains. With relevance feedback, our approach provides additional improvements of over 50%.

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“…As a result, the system took special care of the HumanComputer Interaction (HCI), a research topic that had not been prioritized during the first decade of CBIR systems [23]. The visualization of results was targeted in different studies, focusing on the arrangement of results on the graphic panel [15], proposing alternatives to the classic grid distribution of result thumbnails [22,24], or grouping similar results by applying clustering algorithms [21]. Another interesting topic was the collection of relevance feedback data in a simple and intuitive interface.…”

Section: Related Workmentioning

confidence: 99%

System architecture of a web service for Content-Based Image Retrieval

Giró-i-Nieto

Ventura

Pont-Tuset

et al. 2010

Proceedings of the ACM International Conference on Image and Video Retrieval

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This paper presents the design and implementation of a Content-Based Image Retrieval system where queries are formulated by visual examples through a graphical interface and content can be remotely accessed through web sevices. Visual descriptors and similarity measures implemented in this work are mostly defined in the MPEG-7 standard, while textual metadata is coded according to the Dublin Core specifications. Regarding the web service aspect, the presented solution is composed of two parts, a client running a graphical user interface to pose the queries and a server where the search engine explores an image repository. The separation of the user interface and the search engine follows a Service as a Software (SaaS) model, a type of cloud computing design where a single core system is online and available to authorized clients. The proposed architecture follows the REST software architecture and HTTP protocol for communications, two solutions that combined with metadata coded in RDF, make the proposed system ready for its integration in the semantic web.

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The Mediamill Semantic Video Search Engine

Cited by 34 publications

References 7 publications

A Multidisciplinary Approach to Unlocking Television Broadcast Archives

A Multidisciplinary Approach to Unlocking Television Broadcast Archives

Zero-shot video retrieval using content and concepts

System architecture of a web service for Content-Based Image Retrieval

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