Abstract
There are still major challenges in the area of automatic indexing and retrieval of digital data. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. Research has been ongoing for several years in the field of ontological engineering with the aim of using ontologies to add knowledge to information. In this chapter we describe the architecture of a system designed to semi-automatically and intelligently index huge repositories of special effects video clips. The indexing is based on the semantic content of the video clips and uses a network of scalable ontologies to represent the semantic content to further enable intelligent retrieval.
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Badii, A., Lallah, C., Zhu, M., Crouch, M. (2011). Semi-automatic Knowledge Extraction, Representation, and Context-Sensitive Intelligent Retrieval of Video Content Using Collateral Context Modelling with Scalable Ontological Networks. In: Lin, W., Tao, D., Kacprzyk, J., Li, Z., Izquierdo, E., Wang, H. (eds) Multimedia Analysis, Processing and Communications. Studies in Computational Intelligence, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19551-8_17
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