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Metric learning based on attribute hypergraph

In this paper, we propose an improved attribute hypergraph learning framework and adapt it for metric learning. Under the attribute hypergraph, each image is abstracted as a vertex and is contained in some hyperedges, each of which represents an attribute. The learned attribute hypergraph mines the correlation among multiple facial attributes and serves to reform the topology of the image similarity relationship in a database defined by the distance metric. By combining the merits of attribute and hypergraph, the reformed distance metric from the attribute hypergraph learning framework is able to capture the intrinsic information of the data set. Extensive experimental results on LFW data set confirm the effectiveness of the improved attribute hypergraph learning framework.