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Sparse Affinity Propagation for Image Analysis

机译：稀疏亲和传播用于图像分析

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It is challenging to find a small set of data points, so-called exemplars or landmarks, that are nicely representative of itself and other data points. Affinity propagation (AP) is an effective algorithm that identifies exemplars among data points by recursively sending realvalued messages between pairs of data points. AP calculates the message using the similarity among data points. Hence, the construction of similarity graph lies in the heart of the AP algorithm. A common choice for similarity is negative Euclidean distance. However, most data points, especial high-dimensional data, lies into the non-Euclidean space such that Euclidean distance cannot capture the real structure of data. Moreover, Euclidean distance is sensitive to noise and outliers such that the performance of the algorithm will be degraded when data are grossly corrupted. In this paper, we propose an algorithm, named as Sparse Affinity Propagation (SAP), which adopts sparse representation coefficient to depict the relationship among data points. For a given data set, SAP calculates the sparse representation for each data point by solving a convex problem; and then, builds a similarity graph using the representation coefficient; after that, obtains the exemplars by performing AP over the sparse similarity graph. To verify the efficacy of our algorithm, we carried out numerous experiments in the context of data summarization. Empirical studies show that SAP is superior to AP and other baseline algorithms for image analysis in accuracy and robustness.
机译：很难找到一小组能很好地代表自身和其他数据点的数据点，即所谓的范例或地标。相似性传播（AP）是一种有效的算法，可通过在数据点对之间递归发送实值消息来识别数据点中的示例。 AP使用数据点之间的相似度来计算消息。因此，相似度图的构建是AP算法的核心。相似性的常见选择是负欧氏距离。但是，大多数数据点，尤其是高维数据，都位于非欧几里得空间中，因此欧几里得距离无法捕获数据的真实结构。此外，欧几里得距离对噪声和离群值敏感，因此当数据严重损坏时，算法的性能将降低。在本文中，我们提出了一种称为稀疏亲和度传播（SAP）的算法，该算法采用稀疏表示系数来描述数据点之间的关系。对于给定的数据集，SAP通过解决凸问题来计算每个数据点的稀疏表示。然后，利用表示系数建立相似度图。之后，通过在稀疏相似度图上执行AP获得示例。为了验证我们算法的有效性，我们在数据汇总的背景下进行了许多实验。实证研究表明，SAP在图像分析的准确性和鲁棒性方面优于AP和其他基线算法。

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