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首页> 外文期刊>International Journal of Multimedia Information Retrieval >DHFML: deep heterogeneous feature metric learning for matching photograph and cartoon pairs
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DHFML: deep heterogeneous feature metric learning for matching photograph and cartoon pairs

机译：DHFML：匹配照片和卡通对的深度异构特征度量学习

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摘要

We study the problem of retrieving cartoon faces of celebrities given their real face as a query. We refer to this problem as Photo2Cartoon. The Photo2Cartoon problem is challenging since (i) cartoons vary excessively in style and (ii) modality gap between real and cartoon faces is large. To address these challenges, we present a discriminative deep metric learning approach designed for matching cross-modal faces and showcase Photo2Cartoon. The proposed approach learns a nonlinear transformation to project real and cartoon face pairs into a common subspace where distance between positive pairs becomes smaller as compared to distance between negative pairs.We evaluate our method on two public benchmarks, namely IIIT-CFW and Viewed Sketch, and show superior retrieval results as compared to related methods.
机译：我们研究了将他们真正的面孔作为查询给名人的卡通面的问题。 我们将此问题称为photo2cartoon。 Photo2Cartoon问题是挑战，因为（i）漫画在风格过度变化和（ii）真实和卡通面之间的模态差异很大。 为了解决这些挑战，我们提出了一种歧视的深度度量学习方法，用于匹配跨模板面和展示照片2 Cartoon。 所提出的方法将非线性转换学习到项目真实和卡通面对的公共子空间，其中正对与负对对之间的距离相比，正对之间的距离变小。我们在两个公共基准上评估我们的方法，即IIIT-CFW和查看草图， 与相关方法相比，显示出卓越的检索结果。

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