A GA-Based Solution for the Combination Optimization in the Contour Formation

Object recognition method based on Geometric characteristics is a key method to solve the visual pattern recognition problem. Contour feature is one of the most important geometric clues. Biological visual cortex can get fragmentary information of the object edges. How to combine the fragments to a longer, more complete contour becomes a key basic problem. Genetic algorithm is usually used to solve combination optimization problems. This paper uses a new kind of gene encoding based on graph structure and an improved algorithm to combine the short line segments. The experimental results show that using the formatting long contour lines can improve the performance and the long contour lines can promote the realization of recognition invariance. Meanwhile, there is no loss of the information and it takes less space to store the images. Large contour features have great significance for the definition of object structured semantics, the explicit definition of the knowledge of the object recognition and realization of the process of top-down processing.