RI: Small: Hierarchical Feature Learning by Heterogeneous Networks with Application to Face Verification

RI：小型：异构网络的分层特征学习及其在人脸验证中的应用

• 批准号: 1318971
• 负责人: Thomas Huang
• 金额: $ 40.86万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318971&HistoricalAwards=false
• 关键词: RI; Small; Hierarchical; Feature; Learning

Learning good features is a key to computer vision problems such as recognizing human faces, and understanding scenes. Many computer vision researchers learn features by providing a semantic label for each image in a large database, limiting the amount of information per image to a few bits. Others learn features by identifying common patterns found in images such as lines, blobs, and more complicated shapes, but ignoring semantic information. This project develops algorithms to learn features that are common in images and also predict the semantics of images at various spatial scales using a new type of deep neural network called Heterogeneous Networks. The developed algorithms allow the incorporation of semantic information at intermediate layers. The algorithms developed can not only change how features are learned but also indicate how to scale feature learning to giant datasets of millions of images. The research team addresses challenging problems in human face verification using NCSA's petascale supercomputer, Blue Waters, and two large scale (millions of images) image data sets. The research of this projected is integrated with both undergraduate and graduate education. The results obtained from this project are applicable to a wide range of applications in computer vision and pattern recognition. The research team plans to release algorithms and face data sets collected in this project to research communities once they are finished.

学习好的特征是计算机视觉问题的关键，例如识别人脸和理解场景。许多计算机视觉研究人员通过为大型数据库中的每个图像提供语义标签来学习特征，将每个图像的信息量限制在几位。其他人通过识别图像中的常见模式（如线条，斑点和更复杂的形状）来学习特征，但忽略语义信息。该项目开发算法来学习图像中常见的特征，并使用一种称为异构网络的新型深度神经网络来预测各种空间尺度下的图像语义。开发的算法允许在中间层的语义信息的合并。 开发的算法不仅可以改变特征的学习方式，还可以指示如何将特征学习扩展到数百万图像的巨型数据集。 该研究小组使用NCSA的千万亿次超级计算机Blue沃茨和两个大规模（数百万张图像）图像数据集解决人脸验证中的挑战性问题。该项目的研究与本科和研究生教育相结合。本研究成果可广泛应用于计算机视觉和模式识别领域。研究团队计划在完成后将该项目中收集的算法和人脸数据集发布给研究社区。