Rethinking the Rotation Invariance of Local Convolutional Features for Content-Based Image Retrieval
Rethinking the Rotation Invariance of Local Convolutional Features for Content-Based Image Retrieval
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DOI：
10.1587/transinf.2020edp7017
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发表时间：
2021-01
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影响因子：
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通讯作者：
Long Zhao;Yu Wang;Jien Kato
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文献类型：
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作者：
Long Zhao;Yu Wang;Jien Kato
最近，使用卷积神经网络（CNN）计算的局部特征显示出良好的图像检索性能。由CNN获得的局部卷积特征（LC特征）被设计为平移不变，然而，它们对旋转扰动本质上是敏感的。这导致检索任务中的误判。在这项工作中，我们的目标是提高对图像旋转的LC功能的鲁棒性。为了做到这一点，我们进行了彻底的实验评估的三个候选人的抗旋转策略（模型中的数据增强，模型中的功能增强，和后模型功能增强），在两种旋转攻击（数据集攻击和查询攻击）。在训练过程中，我们实现了一个数据增强协议和网络增强方法。在测试过程中，我们开发了一种局部变换卷积（LTC）特征提取方法，并在不同的网络配置上对其进行了评估。我们最终得到了一系列具有稳定定量支持的良好实践，从而为图像检索中计算具有高旋转不变性的LC特征提供了最佳策略。
SUMMARY Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. The local convolutional features obtained by the CNNs (LC features) are designed to be translation invariant, however, they are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of three candidate anti-rotation strategies (in-model data augmentation, in-model feature augmentation, and post-model feature augmentation), over two kinds of rotation attack (dataset attack and query attack). In the training procedure, we implement a data augmentation protocol and network augmentation method. In the test procedure, we develop a local transformed convolutional (LTC) feature extraction method, and evaluate it over different network configurations. We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.