SUBMITTED TO IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Genetic Algorithms for Scene Interpretationfrom Prototypical Semantic Description
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چکیده
Use of a genetic algorithm assumes the existence of a gure of merit called tness, for which there is a value for every candidate solution. The tness must be measurable over the representation of the solution by means of a computable function. The tness function is, in most cases, independent of the other factors, including the algorithm used. Often, the tness 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 tness is diicult 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.
منابع مشابه
To Appear in Ieee Transactions on Evolutionary Computation 1 Designing Classifier Fusion Systemsby Genetic
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تاریخ انتشار 1998