Search (6 results, page 1 of 1)

0.004371658 = product of:
  0.017486632 = sum of:
    0.017486632 = weight(_text_:information in 4228) [ClassicSimilarity], result of:
      0.017486632 = score(doc=4228,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2850541 = fieldWeight in 4228, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=4228)
  0.25 = coord(1/4)

Abstract

Indexing remains one of the most popular tools provided by digital libraries to help users identify and understand the characteristics of the information they need. Despite extensive studies of the problem of automatic index construction for text-based digital libraries, the construction of multimedia digital libraries continues to represent a challenge, because multimedia objects usually lack sufficient text information to ensure reliable index learning. This research attempts to tackle the problem of automatic index construction for multimedia objects by employing Web usage logs and limited keywords pertaining to multimedia objects. The tests of two proposed algorithms use two different data sets with different amounts of textual information. Web usage logs offer precious information for building indexes of multimedia digital libraries with limited textual information. The proposed methods generally yield better indexes, especially for the artwork data set.

Source

Information processing and management. 46(2010) no.3, S.295-307

0.0036430482 = product of:
  0.014572193 = sum of:
    0.014572193 = weight(_text_:information in 4888) [ClassicSimilarity], result of:
      0.014572193 = score(doc=4888,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23754507 = fieldWeight in 4888, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4888)
  0.25 = coord(1/4)

Abstract

This paper centres on the tools for the management of new digital documents, which are not only textual, but also visual-video, audio or multimedia in the full sense. Among the aims is to demonstrate that operating within the terms of generic Information Retrieval through textual language only is limiting, and it is instead necessary to consider ampler criteria, such as those of MultiMedia Information Retrieval, according to which, every type of digital document can be analyzed and searched by the proper elements of language for its proper nature. MMIR is presented as the organic complex of the systems of Text Retrieval, Visual Retrieval, Video Retrieval, and Audio Retrieval, each of which has an approach to information management that handles the concrete textual, visual, audio, or video content of the documents directly, here defined as content-based. In conclusion, the limits of this content-based objective access to documents is underlined. The discrepancy known as the semantic gap is that which occurs between semantic-interpretive access and content-based access. Finally, the integration of these conceptions is explained, gathering and composing the merits and the advantages of each of the approaches and of the systems to access to information.

Footnote

Bezugnahme auf: Enser, P.G.B.: Visual image retrieval. In: Annual review of information science and technology. 42(2008), S.3-42.

0.003091229 = product of:
  0.012364916 = sum of:
    0.012364916 = weight(_text_:information in 2911) [ClassicSimilarity], result of:
      0.012364916 = score(doc=2911,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.20156369 = fieldWeight in 2911, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2911)
  0.25 = coord(1/4)

Abstract

Various kinds of knowledge organisation, such as thesauri, are routinely used to label or tag multimedia content such as images and music and 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 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.

Content

Beitrag in einem Special issue: The Great Debate: "This House Believes that the Traditional Thesaurus has no Place in Modern Information Retrieval." [19 February 2015, 14:00-17:30 preceded by ISKO UK AGM and followed by networking, wine and nibbles; vgl.: http://www.iskouk.org/content/great-debate].

0.0029745363 = product of:
  0.011898145 = sum of:
    0.011898145 = weight(_text_:information in 2801) [ClassicSimilarity], result of:
      0.011898145 = score(doc=2801,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.19395474 = fieldWeight in 2801, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2801)
  0.25 = coord(1/4)

Abstract

The book covers multimedia ontology in heritage preservation with intellectual explorations of various themes of Indian cultural heritage. The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled. The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums. The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.

Footnote

Rez. in: Annals of Library and Information Studies 62(2015) no.4, S.299-300 (A.K. Das)

LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

0.0029745363 = product of:
  0.011898145 = sum of:
    0.011898145 = weight(_text_:information in 3958) [ClassicSimilarity], result of:
      0.011898145 = score(doc=3958,freq=8.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.19395474 = fieldWeight in 3958, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3958)
  0.25 = coord(1/4)

Abstract

Currently, there is no structured data standard for representing elements commonly found in transmedia fictional worlds. Although there are websites dedicated to individual universes, the information found on these sites separate out the various formats, concentrate on only the bibliographic aspects of the material, and are only searchable with full text. We have created an ontological model that will allow various user groups interested in transmedia to search for and retrieve the information contained in these worlds based upon their structure. We conducted a domain analysis and user studies based on the contents of Harry Potter, Lord of the Rings, the Marvel Universe, and Star Wars in order to build a new model using Ontology Web Language (OWL) and an artificial intelligence-reasoning engine. This model can infer connections between transmedia properties such as characters, elements of power, items, places, events, and so on. This model will facilitate better search and retrieval of the information contained within these vast story universes for all users interested in them. The result of this project is an OWL ontology reflecting real user needs based upon user research, which is intuitive for users and can be used by artificial intelligence systems.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2771-2782

0.0021033147 = product of:
  0.008413259 = sum of:
    0.008413259 = weight(_text_:information in 2743) [ClassicSimilarity], result of:
      0.008413259 = score(doc=2743,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.13714671 = fieldWeight in 2743, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2743)
  0.25 = coord(1/4)

Abstract

Awareness of another's activity is an important aspect of facilitating collaboration between users, enabling an "understanding of the activities of others" (Dourish & Bellotti, 1992). In this paper we investigate the role of awareness and its effect on search performance and behaviour in collaborative multimedia retrieval. We focus on the scenario where two users are searching at the same time on the same task, and via an interface, can see the activity of the other user. The main research question asks: does awareness of another searcher aid a user when carrying out a multimedia search session? To encourage awareness, an experimental study was designed where two users were asked to compete to find as many relevant video shots as possible under different awareness conditions. These were individual search (no awareness), Mutual awareness (where both users could see the other's search screen), and unbalanced awareness (where one user is able to see the other's screen, but not vice-versa). Twelve pairs of users were recruited, and the four worst performing TRECVID 2006 search topics were used as search tasks, under four different awareness conditions. We present the results of this study, followed by a discussion of the implications for multimedia information retrieval systems.

Source

Information processing and management. 48(2012) no.1, S.32-46