Abstract
The fitness function used in a GA must be measurable over the representation of the solution by means of a computable function. Often, the fitness is an estimation of the nearness to an ideal solution or the distance from a default solution. In image scene interpretation, the solution takes the form of a set of labels corresponding to the components of an image and its fitness is difficult to conceptualize in terms of distance from a default or nearness to an ideal. Here we describe a model in which a semantic net is used to capture the salient properties of an ideal labeling. Instantiating the nodes of the semantic net with the labels from a candidate solution (a chromosome) provides a basis for estimating a logical distance from a norm. This domain-independent model can be applied to a broad range of scene-based image analysis tasks.
This work was supported in part by a grant from NASA/Goddard Space Flight Center, #NAG5-8570 and in part by DoD EPSCoR and the State of Louisiana under grant F49620-98-1-0351.
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References
C. A. Ankenbrandt, B. P. Buckles, and F. E. Petry, “Scene recognition using genetic algorithms with semantic nets”, Pattern Recognition Letters, vol. 11, no. 4, pp. 285–293, 1990.
B. P. Buckles and F. E. Petry, Eds., Genetic Algorithms, IEEE Computer Society Press, 1992.
B. Bhanu, S. Lee, and J. Ming, “Self-optimizing image segmentation system using a genetic algorithm”, in Proceedings of the Fourth International Conference on Genetic Algorithms, R.K. Belew and L.B. Booker, Eds., San Mateo, CA, 1991, pp. 362–369, Morgan Kaufmann.
S. M. Bhandarkar and H. Zhang, “Image segmentation using evolutionary computation”, IEEE Trans. on Evolutionary Computation, vol. 3, no. 1, pp. 1–21, apr 1999.
R. Tönjes, S. Growe, J. Bückner, and C.-E. Liedtke, “Knowledge-based interpretation of remote sensing images using semantic nets”, Photogrammetric Engineering & Remote Sensing, vol. 65, no. 7, pp. 811–821, jul 1999.
J. Bala, K. DeJong, and P. Pachowicz, “Using genetic algorithms to improve the performance of classification rules produced by symbolic inductive methods”, in Proceedings of 6th International Symposium Methodologies for Intelligent Systems ISMIS’91, Z. W. Ras and M. Zemankova, Eds., Charlotte, NC, 16–19 Oct 1991, pp. 286–295, Springer-Verlag, Berlin, Germany.
S. Truve, “Using a genetic algorithm to solve constraint satisfaction problems generated by an image interpreter”, in Theory and Applications of Image Analysis. Selected Papers from the 7th Scandinavian Conference, Aalborg, Denmark, P. Johansen and S. Olsen, Eds. Aug, 13–16 1991, pp. 133–147, World Scientific.
A. Hill and C. J. Taylor, “Model-based image interpretation using genetic algorithms”, Image and Vision Computing, vol. 10, no. 5, pp. 295–300, Jun 1992.
D. B. Fogel, “Evolutionary programming for voice feature analysis”, in Proceedings of 23rd Asilomar Conference on Signals, Systems, and Computers, oct 1989, pp. 381–383.
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Prabhu, D., Buckles, B.P., Petry, F.E. (2000). Scene Interpretation using Semantic Nets and Evolutionary Computation. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_4
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DOI: https://doi.org/10.1007/3-540-45561-2_4
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