Abstract
The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB.
Abstract (translated)
深度学习带来的最新进展可以提高图像检索任务的性能。通过卷积神经网络(CNN)中可用的许多卷积层,可以从评估的图像中获得特征的层次结构。在每一步,提取的补丁都比以前的级别小,更具代表性。根据这一想法,本文介绍了一种应用于从预训练CNN中提取的特征图的新检测器。具体来说,这种方法可以增加功能的数量,以提高聚合算法的性能,如最着名和最常用的VLAD嵌入。所提出的方法在不同的公共数据集上进行测试:Holidays,Oxford5k,Paris6k和UKB。
URL
https://arxiv.org/abs/1808.05022