In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.

中文翻译：

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一种使用近似随机毯和线过程能量的新型在线变化点检测

在本文中，我们专注于分段常数信号中的变化点检测问题。这个问题是一些应用的核心，例如人类活动分析、语音或图像分析以及遗传学中的异常检测。我们提出了一种用于在线变化点检测的新型窗口滑动算法。所提出的方法考虑了代表信号及其噪声观察的全局马尔可夫随机场 (MRF) 的局部覆盖。对于每个窗口，我们定义并解决局部能量最小化问题，以推断 MRF 图每条边上的梯度。然后通过激活函数处理梯度以过滤弱特征并产生最终的跳跃。我们通过将其运行时间和几个检测指标与最先进的算法进行比较来证明我们方法的有效性。