Scene Interpretation Using Semantic Nets and Evolutionary Computation

Authors:
D. Prabhu;Bill P. Buckles;Frederick E. Petry
Affiliations:
-;-;-
Venue:
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Year:
2000

Citing 5
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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.