Change-point problems which originated from the field of quality control have become an important area of research. Although existing methods have been successful in detecting change-points, most of them require the underlying data to follow a specific distribution. Heuristically speaking, those methods only perform well when the data set is hypothesised to follow a normal distribution. In this paper, instead of traditional statistical inference, we propose a new algorithm from a shape perspective, which provides a more robust approach to addressing change-point problems. Our new algorithm will define a novel statistic based on shape context, a rich local shape descriptor, to replace the CUSUM test statistic considered by traditional methods. In addition, some areas which do not have change-points are abandoned through segmentation and screening, reducing computational complexity and increasing available storage. At the same time, we introduce the idea of peak recognition, which increases the robustness and effectiveness of the detection. The experimental results demonstrate that the proposed algorithm significantly outperforms some other methods with regard to accuracy and efficiency, especially when a longer time series is under study. We include analyses of two real-world data sets which demonstrate the practical effectiveness of this algorithm.

源于质量控制领域的变更点问题已成为重要的研究领域。尽管现有方法已成功检测出变更点，但大多数方法都要求基础数据遵循特定的分布。试探性地讲，这些方法仅在假设数据集遵循正态分布时才表现良好。在本文中，我们从形状的角度提出了一种新的算法，而不是传统的统计推断，它提供了一种更强大的方法来解决变更点问题。我们的新算法将基于形状上下文（丰富的局部形状描述符）定义一个新颖的统计量，以取代传统方法考虑的CUSUM测试统计量。此外，通过分段和筛选，放弃了一些没有变更点的区域，降低计算复杂度并增加可用存储量。同时，我们介绍了峰识别的概念，它提高了检测的鲁棒性和有效性。实验结果表明，该算法在准确性和效率上明显优于其他方法，尤其是在研究较长时间序列时。我们包括对两个真实数据集的分析，这些数据证明了该算法的实际有效性。特别是在研究更长的时间序列时。我们包括对两个真实数据集的分析，这些数据证明了该算法的实际有效性。特别是在研究更长的时间序列时。我们包括对两个真实数据集的分析，这些数据证明了该算法的实际有效性。