Evolutionary Tabu Search For Geometric Primitive Extraction

Abstract

Many problems in computer vision can be formulated as an optimization problem. Developping the efficient global optimizational technique adaptive to the vision proplem becomes more and more important. In this paper, we present a geometric primitive extraction method, which plays a crucial role in content-based image retrieval and other vision problems. We formulate the problem as a cost function minimization problem and we present a new optimization technique called Evolutionary Tabu Search (ETS). Genetic algorithm and Tabu Search Algorithm are combined in our method. Specificly, we incorporates “the survival of strongest” idea of evolution algorithm into tabu search. In experiments, we use our method for shape extraction in images and compare our method with other three global optimization methods including genetic algorithm, simulated Annealing and tabu search. The results show that the new algorithm is a practical and effective global optimization method, which can yield good near-optimal soultions and has better convergence speed.