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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].

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Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).