The objective of this paper is the effective transfer of the Convolutional
Neural Network (CNN) feature in image search and classification.
Systematically, we study three facts in CNN transfer. 1) We demonstrate the
advantage of using images with a properly large size as input to CNN instead of
the conventionally resized one. 2) We benchmark the performance of different
CNN layers improved by average/max pooling on the feature maps. Our observation
suggests that the Conv5 feature yields very competitive accuracy under such
pooling step. 3) We find that the simple combination of pooled features
extracted across various CNN layers is effective in collecting evidences from
both low and high level descriptors. Following these good practices, we are
capable of improving the state of the art on a number of benchmarks to a large
margin.

本研究旨在有效转移卷积神经网络（CNN）在图像搜索和分类中的特征。通过系统研究 CNN 转移的三个要点，本研究得出了使用适当大小的图像作为 CNN 输入的优势，使用平均 / 最大池化改善特征映射的不同 CNN 层的性能的结果以及在不同 CNN 层中提取集合特征的简单组合有效地从低层次和高层次描述符中收集证据等结论。遵循这些良好实践，本研究在多个基准测试中显著提高了最先进技术水平。