Knowledge-Assisted Media Analysis for Interactive Multimedia Applications
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Editorial Knowledge-Assisted Media Analysis for Interactive Multimedia Applications E. Izquierdo,1 Hyoung Joong Kim,2 and Thomas Sikora3 1 Department
of Electronic Engineering, Queen Mary, University of London, Mile End Road, London E1 4NS, UK of Control and Instrumentation Engineering, Kangwon National University, 192 1 Hyoja2 Dong, Kangwon Do 200 701, South Korea 3 Communication Systems Group, Technical University Berlin, Einstein Ufer 17, 10587 Berlin, Germany 2 Department
Received 30 December 2007; Accepted 30 December 2007 Copyright © 2007 E. Izquierdo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Advances in technologies for new forms of interactive multimedia services are driving the emergence of a digital world which is transforming all aspects of how people consume and interact with digital content. This emergent digital world is characterised by online access to knowledge resources and services independently from location and time. Here, digital services evolve in response to user behaviour, and technology adapts itself to user needs. As a consequence, new forms of interactive user-centred multimedia services materialise originating in turn new business models and economic growth. These services are underpinned by the confluence of different research fields including knowledge management, data mining, and signal processing. The convergence of these areas is the key to many applications including interactive TV, networked medical imaging, visionbased surveillance, and multimedia visualisation, navigation, search, and retrieval. The latter is a crucial application since the exponential growth of audiovisual data, along with the critical lack of tools to record the data in a well-structured form, is rendering vast portions of available useless content. This special issue reports the work related to the development of innovative paradigms and tools that are driving technological advances and producing new interactive knowledge-assisted multimedia services. After a thorough review process, a total of nine papers were selected. The first three papers address the challenging problem of analysis for annotation and retrieval. In their paper, C.-C. Chiang et al. propose a learning state approach for image retrieval. The authors design a scheme of region-based image representation based on concept units, which are integrated with different types of feature spaces and with different region scales of image segmentation. In the second paper by
Q. Zhang and E. Izquierdo, an object-oriented approach for semantic-based image retrieval is presented. The goal is to identify key patterns of specific objects in the training data and to use them as object signatures. Two important aspects of semantic-based image retrieval are considered: retrieval of images containing a given semantic concept and fusion of different low-level features to achieve higher d
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