基于混合粒子群算法的运动估计研究

针对块匹配运动估计算法中传统搜索方法的不足,提出了一种新的基于混合粒子群的块匹配运动估计算法。在保留系统随机搜索性能的同时根据运动矢量特性合理地设计初始搜索种群,并通过混沌差分进化搜索协同粒子群算法迭代寻优,混沌序列用于优化差分变异算子,以提高算法的精细搜索能力。通过相同点检测技术和恰当的终止计划有效地降低了系统的运算复杂度。经实验测试与验证,该算法在搜索质量和运算复杂度中达到了一种动态平衡的状态,其整体性能高于传统的快速运动估计算法,效果更逼近于穷举搜索法。

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The multiclass classification SVM based on the combination of binary tree structures has a small number of binary SVMs, and can avoid the occurrence of inseparable and repellent regions. Since the combination methods of multiclass classification SVM based on binary tree structure lack specific evaluation criteria for category combination, we propose a multiclass classification SVM structure evaluation method based on information gain (IG) ratio of classification attributes, define the IG ratio of classification attributes, and divide multiple classes into left category and right category. We calculate the IG ratio dependent on the classification attribute of variables for each possible combination of categories, and take the maximum value of the IG ratio as the evaluation criterion of this combination. Empirical analysis on Iris in the UCI database shows that the proposed method has a high recognition rate for multiclass classification SVM when the maximum value is taken as the evaluation criterion .