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Knowledge-Assisted Image and Video Analysis

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Definition

Knowledge-assisted image and video analysis refers to the use of domain-specific prior knowledge about images or video.

Segmentation of images and video is generally an ill-posed problem, i.e. for a given natural image or image sequence, there exists no unique solution to the segmentation problem. In order for the segmentation problem to be transformed to a well-posed one, restriction of the number of admissible solutions is necessary. To this end, the introduction of prior knowledge to the segmentation procedure, leading to the development of domain-specific knowledge-assisted analysis techniques, has been proposed.

Prior knowledge for a domain (e.g. F1 racing) typically includes the important objects that can be found in any given image or frame belonging to this domain (e.g. car, road, grass, sand, etc.), their characteristics (e.g. corresponding color models) and any relations between them. See example in Fig. 1. Given this knowledge, there exists the well-posed problem...

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References

  1. M. Zhang, L.O. Hall, and D.B. Goldgof, “A Generic Knowledge-Guided Image Segmentation and Labeling System Using Fuzzy Clustering Algorithms,” IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 32, No. 5, October 2002, pp. 571–582.

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  2. V. Mezaris, I. Kompatsiaris, and M.G. Strintzis, “A Knowledge-Based Approach to Domain-Specific Compressed Video Analysis,” Proceedings of the IEEE International Conference on Image Processing, October 2004, Singapore, pp. 341–344.

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  3. N. Voisine, S. Dasiopoulou, V. Mezaris, E. Spyrou, T. Athanasiadis, I. Kompatsiaris, Y. Avrithis, and M.G. Strintzis, “Knowledge-Assisted Video Analysis Using a Genetic Algorithm,” Proceedings of the Workshop on Image Analysis For Multimedia Interactive Services, April 2005, Montreux, Switzerland.

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© 2008 Springer-Verlag

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(2008). Knowledge-Assisted Image and Video Analysis. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_103

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