Abstract
Metric learning is a significant factor for media retrieval. In this paper, we propose an attribute label enhanced metric learning model to assist face image retrieval. Different from general cross-media retrieval, in the proposed model, the information of attribute labels are embedded in a hypergraph metric learning framework for face image retrieval tasks. The attribute labels serve to build a hypergraph, in which each image is abstracted as a vertex and is contained in several hyperedges. The learned hypergraph combines the attribute label to reform the topology of image similarity relationship. With the mined correlation among multiple facial attributes, the reformed metrics incorporates the semantic information in the general image similarity measure. We apply the metric learning strategy to both similarity face retrieval and interactive face retrieval. The proposed metric learning model effectively narrows down the semantic gap between human and machine face perception. The learned distance metric not only increases the precision of similarity retrieval but also speeds up the convergence distinctively in interactive face retrieval.
Highlights
The rapid increase of available face images in media, security, and Internet comes up with enormous requirements for retrieval applications
The regular measure of precision in information retrieval is adopted to assess the performance of the metric learning model with regard to the parameter λ
4.3 Coherence analysis In interactive face retrieval experiments, we evaluate the effectiveness of the reformed distance metric by the retrieval convergence speed and by evaluating the semantic gap between human and computer face perception
Summary
The rapid increase of available face images in media, security, and Internet comes up with enormous requirements for retrieval applications. As in most media retrieval tasks, the similarity measure is an essential step in face retrieval. Image similarity is measured with distance metrics between the feature vectors of a pair of images. Such measurements rely mainly on the feature extraction strategies, which cannot incorporate sufficient semantic information such as attribute labels. It is hard to use simple distance metrics to annotate complex semantic correlations in media database and accomplish high-level media retrieval tasks. As a more advanced technique, metric learning takes advantage of more supervision information to refine the general distance metric and reveal the hidden correlations in the retrieval set of media [1,2,3]. Metric learning has made great achievements in image classification [4, 5] and pedestrian re-identification [6]
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