基于全局与局部形状特征融合的形状识别算法

Shape recognition algorithm based on fusion of global and local properties

经典的全局形状识别算法虽然高效,但在处理形变方面存在不足。局部形状识别算法拥有良好的检索率,但在辨别力方面的效果却有待提高。为解决上述问题,本文提出一种基于特征点分类的融合框架,该框架不仅融合了全局与局部算法的优势,还弥补了二者的不足。一些经典的形状识别算法采用提取特征点的方式来构建形状特征直方图,本文在此基础上,将提取到的特征点进一步分类,针对不同类别的特征点集合采用不同的形状识别算法进行描述,并将匹配结果进行融合,充分发挥了全局与局部算法的优势。实验结果表明,本文提出的框架能够有效结合不同算法实现形状的识别并获得更好的效果。

Although the classical global shape recognition algorithm is efficient,it is not good enough to deal with the deformation.The local shape recognition algorithm has a good retrieval rate,however,the discriminability still needs improvement.To solve these problems,a fusion framework based on the classification of characteristic points is proposed,which not only takes the advantages of global and local shape recognition algorithms,but also makes up for the lacks of the two algorithms.Some classic shape recognition algorithms build the shape feature histogram by extracting the characteristic points.Based on this,these points are further classified that different shape recognition algorithms are applied to different kinds of points.The matching results are then fused to make full used of the advantages of the global and local shape feature descriptors.Experiment results show that the proposed framework can effectively combine different algorithms to achieve the shape recognition and get better results.