Visual image retrieval

Enser, Peter G. B.

doi:10.1002/aris.2008.1440420108

Cited by 26 publications

(26 citation statements)

References 78 publications

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“…A time-and personnel-independent approach is the automatic, content-based indexing of videos (Aigrain et al, 1996;Colombo et al, 1999;Del Bimbo, 1999;Enser, 2008a;Enser, 2008b;Gupta and Jain, 1997). In order to make content-based retrieval (Stock, 2007a, Ch.…”

Section: Video Retrieval (Research Question 1)mentioning

confidence: 99%

Collective indexing of emotions in videos

Knautz

Stock

2011

Journal of Documentation

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Purpose -The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval? Design/methodology/approach -The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed. Findings -The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document -if indeed there is any feeling at all. Originality/value -This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.

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Section: Video Retrieval (Research Question 1)mentioning

confidence: 99%

Collective indexing of emotions in videos

Knautz

Stock

2011

Journal of Documentation

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“…videos, apart from that provided by the Library of Congress (Taves et al, 1998), forms/genres for films/videos (Library of Congress N.D.a), the FIAF cataloguing rules for film archives (Harrison 1991) and the Multimedia Content Description Standard MPEG-7 (MPEG-7 N.D.). A fuller review of schemes can be found in (Enser 2008b).…”

Section: Use Of Knowledge Organisation In Multimedia Information Retrmentioning

confidence: 99%

Knowledge Organisation and its Role in Multimedia Information Retrieval

MacFarlane¹

2016

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Various kinds of knowledge organisation (such as thesauri) are routinely used to label or tag multimedia content such as images and music, to support information retrieval i.e. user search for such content. In this paper we outline why this is the case, in particular focusing on the semantic gap between content and concept based multimedia retrieval. We survey some indexing vocabularies used for multimedia retrieval, and argue that techniques such as thesauri will be needed for the foreseeable future in order to support users in their need for multimedia content. In particular we argue that Artificial Intelligence (A.I.) techniques are not mature enough to solve the problem of indexing multimedia conceptually, and will not be able to replace human indexers for the foreseeable future. ACKNOWLEDGEMENTS I am very grateful to my colleague Deborah Lee for her advice and links to/on thesauri for music and video, and also to David Bawden in confirming the lack of work in those domains. Thanks also go to Stella Dextre Clarke and Judi Vernau for their very constructive comments on various drafts of the paper.

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“…Some journals only occur in some years, for example, in 2008 the Annual Review of Information Science and Technology and the Journal of Information Science (ARIST has a JCR impact factor of 3.030), traditionally more information science than computer science publications, are in the top ranking. ARIST 2008 had a paper on “Visual image retrieval” by Peter Enser (2008a) which explains its ranking in that year and, likewise, the Journal of Information Science had a paper by the same author (Enser, 2008b) on “The evolution of image retrieval.” These were current “state of the art” papers reviewing progress and some of their content discussed the role of TRECVid but they are clearly a different kind of paper than one by a participant describing new breakthroughs or techniques.…”

Section: Key Questions and Answersmentioning

confidence: 99%

The scholarly impact of TRECVid (2003–2009)

Thornley

Johnson

Smeaton

et al. 2011

J. Am. Soc. Inf. Sci.

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This paper reports on an investigation into the scholarly impact of the TRECVid (TREC Video Retrieval Evaluation) benchmarking conferences between 2003 and 2009. The contribution of TRECVid to research in video retrieval is assessed by analyzing publication content to show the development of techniques and approaches over time and by analyzing publication impact through publication numbers and citation analysis. Popular conference and journal venues for TRECVid publications are identified in terms of number of citations received. For a selection of participants at different career stages, the relative importance of TRECVid publications in terms of citations vis a vis their other publications is investigated. TRECVid, as an evaluation conference, provides data on which research teams 'scored' highly against the evaluation criteria and the relationship between 'top scoring' teams at TRECVid and the 'top scoring' papers in terms of citations is analysed. A strong relationship was found between 'success' at TRECVid and 'success' at citations both for high scoring and low scoring teams. The implications of the study in terms of the value of TRECVid as a research activity, and the value of bibliometric analysis as a research evaluation tool, are discussed.

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Visual image retrieval

Cited by 26 publications

References 78 publications

Collective indexing of emotions in videos

Collective indexing of emotions in videos

Knowledge Organisation and its Role in Multimedia Information Retrieval

The scholarly impact of TRECVid (2003–2009)

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