Abstract
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 colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of 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 presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.
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Osman, T., Thakker, D., Schaefer, G. (2010). Semantics-Based Intelligent Indexing and Retrieval of Digital Images – A Case Study. In: Chbeir, R., Badr, Y., Abraham, A., Hassanien, AE. (eds) Emergent Web Intelligence: Advanced Information Retrieval. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84996-074-8_5
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