基于特征模型的形状识别与聚类算法

Shape recognition and clustering algorithm based on a feature model

形状识别与聚类是人工智能和模式识别领域的重要研究方向。针对形状的不同空间结构特征,通过对形状轮廓点所构完全图的Laplace矩阵的奇异值分解以及图的随机游走模型的分析,建立了描述形状空间结构特征的数学模型。以此模型为基础,定义状态向量,并利用公共数据集的数据,实现了形状的识别与聚类。同时,通过对比实验证明了该文提出的算法具有较好的聚类的效果。

Shape recognition and clustering are the key point in artificial intelligence and pattern recognition.Singular value decomposition（SVD） of Laplacian matrix and the random walk model were analyzed to construct the mathematical model of the shape structural feature.Based on this model and the public data sets,shape clustering was completed using the state-vector.Experimental results illustrate that the performance of the proposed approach is better than that of other compared algorithms.