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首页> 外文期刊>Journal of visual communication & image representation >Learning local feature representation from matching, clustering and spatial transform
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Learning local feature representation from matching, clustering and spatial transform

机译：通过匹配，聚类和空间变换学习局部特征表示

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This paper focuses on learning the local image region representation via deep neural networks. Existing works mainly learn from matched corresponding image patches, with which the learned feature is too sensitive to the individual local patch matching result and cannot handle aggregation based tasks such as image level retrieval. Thus, we propose to use both the matched corresponding image patches and the clustering result as labels for the network training. To resolve the inconsistency between the matched correspondences and clustering results, we propose a semi-supervised iterative training scheme together with a dual margins loss. Moreover, a jointly learned spatial transform prediction network is utilized to obtain better spatial transform invariance of the learned local features. Using SIFT as the label initializer, experimental results show the comparable or even better performance than the hand-crafted feature, which sheds lights on learning local feature representation in an unsupervised or weakly supervised manner. (C) 019 Elsevier Inc. All rights reserved.
机译：本文着重于通过深度神经网络学习局部图像区域表示。现有作品主要是从匹配的对应图像补丁中学习，所学习的特征对各个局部补丁匹配结果过于敏感，无法处理基于聚合的任务，如图像级检索。因此，我们建议使用匹配的对应图像补丁和聚类结果作为网络训练的标签。为了解决匹配的对应关系和聚类结果之间的不一致性，我们提出了一种半监督迭代训练方案，并提出了双重边际损失。此外，联合学习的空间变换预测网络被用来获得学习的局部特征的更好的空间变换不变性。使用SIFT作为标签初始化程序，实验结果显示出与手工制作的功能相当甚至更好的性能，这为以无监督或弱监督的方式学习局部特征表示提供了启示。 （C）019 Elsevier Inc.保留所有权利。

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