Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases

被引:35
作者
Ercoli, Simone [1 ]
Bertini, Marco [1 ]
Del Bimbo, Alberto [1 ]
机构
[1] Univ Florence, Media Integrat & Commun Ctr, I-50139 Florence, Italy
关键词
Convolutional neural network (CNN); hashing; nearest neighbor search; retrieval; SIFT; LEARNING BINARY-CODES; PRODUCT QUANTIZATION; IMAGE; SEARCH; MULTIINDEX;
D O I
10.1109/TMM.2017.2697824
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we present an efficient method for visual descriptors retrieval based on compact hash codes computed using a multiple k-means assignment. The method has been applied to the problem of approximate nearest neighbor (ANN) search of local and global visual content descriptors, and it has been tested on different datasets: three large scale standard datasets of engineered features of up to one billion descriptors (BIGANN) and, supported by recent progress in convolutional neural networks (CNNs), on CIFAR-10, MNIST, INRIA Holidays, Oxford 5K, and Paris 6K datasets; also, the recent DEEP1B dataset, composed by one billion CNN-based features, has been used. Experimental results show that, despite its simplicity, the proposed method obtains a very high performance that makes it superior to more complex state-of-the-art methods.
引用
收藏
页码:2521 / 2532
页数:12
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