Fast Covariance Matching With Fuzzy Genetic Algorithm
Fast Covariance Matching With Fuzzy Genetic Algorithm
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DOI：
10.1109/tii.2011.2172453
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发表时间：
2012-02
影响因子：
12.3
通讯作者：
Xuguang Zhang;Shuo Hu;Dan Chen;Xiaoli Li
中科院分区：
文献类型：
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作者：
Xuguang Zhang;Shuo Hu;Dan Chen;Xiaoli Li
现有的协方差匹配方法由于需要穷举搜索而不适合实时应用。针对这个问题，我们开发了一种基于模糊遗传算法（GA）的新方法来提高协方差匹配的计算效率。该方法利用遗传算法在大图像区域中搜索最优解。为了避免传统遗传算法中经常出现的早熟收敛或局部最优，我们使用模糊推理系统自适应地估计交叉和变异概率，以比使用传统遗传算法更快的速度获得收敛。实验结果表明，该方法可以显着提高协方差匹配的处理速度，同时保持匹配结果几乎不变。所提出的方法的运行时性能比使用穷举搜索的同类方法快八倍甚至更多。
The exiting covariance matching method is not suited for real-time applications due to its demand for exhaustive search. Aiming at this problem, we developed a novel approach based on fuzzy genetic algorithm (GA) to boost the computing efficiency of covariance matching. The approach employs GA in searching for optimal solution in a large image region. To avoid premature convergence or local optimum which often occur in traditional GAs, we use a fuzzy inference system to adaptively estimate the crossover and mutation probabilities to gain convergence in a much higher speed than using a conventional GA. Experimental results show that the proposed approach can significantly improve the processing speed of covariance matching, while keeping the matching results almost unchanged. The runtime performance of the proposed approach is faster than its counterparts using exhaustive search with eight times and more.