计算机科学
可扩展性
图像检索
云计算
加密
卷积神经网络
情报检索
过程(计算)
数据挖掘
人工智能
图像(数学)
计算机视觉
数据库
计算机网络
操作系统
作者
Xin Li,Qinghan Xue,Mooi Choo Chuah
标识
DOI:10.1109/infocom.2017.8056953
摘要
Image retrieval has become an important function in many emerging computer vision applications e.g. online shopping via images, medical health care systems. More and more images are being generated and stored in public clouds. However, recent photo leakage events raise concerns about privacy leaks for images stored in public clouds. In this paper, we present an efficient scalable hierarchical image retrieval system (CASHEIRS) which provides privacy-aware image retrieval feature. CASHEIRS employs transformed Convolutional Neural Network features to improve image retrieval accuracy and an encrypted hierarchical index tree to speed up the query process. Extensive evaluations using Caltech256 and INRIA Holiday datasets show that CASHEIRS is more effective than three existing schemes. We also demonstrate its practicality on a mobile device.
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