Previous work has shown that feature maps of deep convolutional neural
networks (CNNs) can be interpreted as feature representation of a particular
image region. Features aggregated from these feature maps have been exploited
for image retrieval tasks and achieved state-of-the-art performances in recent
years. The key to the success of such methods is the feature representation.
However, the different factors that impact the effectiveness of features are
still not explored thoroughly. There are much less discussion about the best
combination of them.
The main contribution of our paper is the thorough evaluations of the various
factors that affect the discriminative ability of the features extracted from
CNNs. Based on the evaluation results, we also identify the best choices for
different factors and propose a new multi-scale image feature representation
method to encode the image effectively. Finally, we show that the proposed
method generalises well and outperforms the state-of-the-art methods on four
typical datasets used for visual instance retrieval.

本文对多种因素对卷积神经网络（CNN）提取的特征代表性的影响进行了全面评估，提出了一种新的多尺度图像特征表示方法来有效编码图像，并在可视化目标检索的四个典型数据集上表现出了优异的性能。