Automatic index construction for multimedia digital libraries

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 multimed...

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Bibliographic Details
Published in:Information processing & management 2010-05, Vol.46 (3), p.295-307
Main Authors: Hwang, San-Yih, Yang, Wan-Shiou, Ting, Kang-Di
Format: Article
Language:English
Subjects:
Algorithms
Automatic indexing
Cluster indexing
Construction
Content analysis
Data processing
Digital libraries
Digital library
Digital systems
Exact sciences and technology
Indexing
Indexing. Classification. Abstracting
Indexing. Classification. Abstracting. Syntheses
Information and communication sciences
Information and document structure and analysis
Information management
Information processing and retrieval
Information retrieval
Information science. Documentation
Learning
Logs
Multimedia
Multimedia database
Online information retrieval
Sciences and techniques of general use
Studies
Texts
Web usage mining
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Summary: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.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2009.10.006