This paper proposes a fast recognition method based on generalized similarity measure (GSM). The GSM achieves good recognition accuracy for face recognition, but has a scalability problem. Because the GSM method requires the similarity measures between a query and all samples to be calculated, the computational cost for recognition is in proportion to the number of samples. A reasonable approach to avoiding calculating all the similarity measures is to limit the number of samples used for calculation. Although approximate nearest neighbor search (ANNS) methods take this approach, they cannot be applied to the GSM-based method directly because they assume that similarity measure is the Euclidean distance. The proposed method embeds the GSM into the Euclidean distance so that it may be applied in existing ANNS methods. We conducted experiments on face, object, and character datasets, and the results show that the proposed method achieved fast recognition without dropping the accuracy.

中文翻译：

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基于广义相似度测度的快速搜索

提出了一种基于广义相似度量的快速识别方法。GSM实现了良好的面部识别识别精度，但存在可扩展性问题。因为GSM方法需要在查询和要计算的所有样本之间采用相似性度量，所以用于识别的计算成本与样本数量成正比。避免计算所有相似度的合理方法是限制用于计算的样本数。尽管近似最近邻搜索（ANNS）方法采用了这种方法，但是由于它们假定相似性度量是欧几里得距离，因此无法直接应用于基于GSM的方法。所提出的方法将GSM嵌入到欧几里得距离中，以便可以在现有的ANNS方法中应用。