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No Fear of the Dark: Image Retrieval Under Varying Illumination Conditions

机译：无惧黑暗：在各种照明条件下的图像检索

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摘要

Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned. Prior to extracting image descriptors by a convolutional neural network, images are photometrically normalised in order to reduce the descriptor sensitivity to illumination changes. We propose a learnable normalisation based on the U-Net architecture, which is trained on a combination of single-camera multi-exposure images and a newly constructed collection of similar views of landmarks during day and night. We experimentally show that both hand-crafted normalisation based on local histogram equalisation and the learnable normalisation outperform standard approaches in varying illumination conditions, while staying on par with the state-of-the-art methods on daylight illumination benchmarks, such as Oxford or Paris datasets.
机译：手工制作和学习的图像预处理可解决变化的光照条件下的图像检索，例如白天和夜晚的图像。在通过卷积神经网络提取图像描述符之前，对图像进行光度标准化，以降低描述符对照明变化的敏感性。我们提出了一种基于U-Net架构的可学习归一化方法，该规范化方法是结合单相机多次曝光图像和新构建的白天和黑夜类似地标视图集合进行训练。我们通过实验表明，基于局部直方图均衡化的手工标准化和可学习的标准化在变化的照明条件下均优于标准方法，同时与日光照明基准（如牛津或巴黎）上的最新方法保持一致数据集。

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