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Supervised Dimensionality Reduction on Grassmannianfor Image Set Recognition

机译：Grassmannian r n用于图像集识别的监督降维

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摘要

Modeling videos and image sets by linear subspaces has achieved great success in various visual recognition tasks. However, subspaces constructed from visual data are always notoriously embedded in a high-dimensional ambient space, which limits the applicability of existing techniques. This letter explores the possibility of proposing a geometry-aware framework for constructing lower-dimensional subspaces with maximum discriminative power from high-dimensional subspaces in the supervised scenario. In particular, we make use of Riemannian geometry and optimization techniques on matrix manifolds to learn an orthogonal projection, which shows that the learning process can be formulated as an unconstrained optimization problem on a Grassmann manifold. With this natural geometry, any metric on the Grassmann manifold can theoretically be used in our model. Experimental evaluations on several data sets show that our approach results in significantly higher accuracy than other state-of-the-art algorithms.
机译：通过线性子空间对视频和图像集进行建模已在各种视觉识别任务中取得了巨大的成功。然而，由视觉数据构成的子空间总是臭名昭著地嵌入在高维环境空间中，这限制了现有技术的适用性。这封信探讨了提出一种几何感知框架的可能性，该框架可用于在有监督的情况下从高维子空间中获得具有最大判别能力的低维子空间。特别是，我们利用矩阵流形上的黎曼几何和优化技术来学习正交投影，这表明学习过程可以表述为格拉斯曼流形上的无约束优化问题。有了这种自然的几何形状，理论上可以在我们的模型中使用Grassmann流形上的任何度量。对几个数据集的实验评估表明，与其他最新算法相比，我们的方法产生的准确性明显更高。

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  • 来源
    《Neural computation》 |2019年第1期|156-175|共20页
  • 作者

    Liu Tianci; Shi Zelin; Liu Yunpeng;

  • 作者单位

    Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Liaoning, Peoples R China|Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Liaoning, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China|Chinese Acad Sci, Key Lab Optoelect Informat Proc, Shenyang 110016, Liaoning, Peoples R China|Key Lab Image Understanding & Comp Vis, Shenyang 110016, Liaoning, Peoples R China;

    Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Liaoning, Peoples R China|Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Liaoning, Peoples R China|Chinese Acad Sci, Key Lab Optoelect Informat Proc, Shenyang 110016, Liaoning, Peoples R China|Key Lab Image Understanding & Comp Vis, Shenyang 110016, Liaoning, Peoples R China;

    Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Liaoning, Peoples R China|Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110169, Liaoning, Peoples R China|Chinese Acad Sci, Key Lab Optoelect Informat Proc, Shenyang 110016, Liaoning, Peoples R China|Key Lab Image Understanding & Comp Vis, Shenyang 110016, Liaoning, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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