Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return...

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Veröffentlicht in:Computing Jg. 96; H. 7; S. 651 - 668
Hauptverfasser: Osman, Taha, Thakker, Dhavalkumar, Schaefer, Gerald
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.07.2014
Springer Nature B.V
Schlagworte:
Accuracy
Analysis
Annotations
Artificial Intelligence
Case studies
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Digital
Digital broadcasting
Image retrieval
Indexing
Information Systems Applications (incl.Internet)
Keywords
Knowledge management
Markets
Ontology
Queries
Query expansion
Query processing
Retrieval
Search engines
Searches
Searching
Semantic web
Semantics
Software
Software Engineering
Studies
Semantic matching
Image retrieval
Keyword search
Query expansion
Knowledge management
68Q55
Ontology engineering
68P20
ISSN:0010-485X, 1436-5057
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.
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ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-013-0350-0