.png){kind=icon}
.png){kind=icon}
Return
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
.png){kind=icon}
OA
AI
DOI：10.1109/TMM.2017.2697824.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Convolutional neural network (CNN)
hashing
nearest neighbor search
retrieval
SIFT
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.7
Papers:
4.5K
Citations:
2.4W