Semantic Image Analysis Using a Learning Approach and Spatial Context
In this paper, a learning approach coupling Support Vector Machines (SVMs) and a Genetic Algorithm (GA) is presented for knowledge-assisted semantic image analysis in specific domains. Explicitly defined domain knowledge under the proposed approach includes objects of the domain of interest and their spatial relations. SVMs are employed using low-level features to extract implicit information for each object of interest via training in order to provide an initial annotation of the image regions based solely on visual features. To account for the inherent visual information ambiguity spatial context is subsequently exploited. Specifically, fuzzy spatial relations along with the previously computed initial annotations are supplied to a genetic algorithm, which uses them to decide on the globally most plausible annotation. In this work, two different fitness functions for the GA are tested and evaluated. Experiments with outdoor photographs demonstrate the performance of the proposed approaches.
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