Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
This addresses the problem of scalable approximate nearest neighbor search for visual content retrieval, which is incremental as it builds on existing hash code and k-means techniques.
The paper tackles efficient visual descriptor retrieval in large-scale databases by proposing a method using compact hash codes via multiple k-means assignment, achieving superior performance over complex state-of-the-art methods on datasets up to one billion descriptors.
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 public datasets of up to one billion descriptors (BIGANN) and, supported by recent progress in convolutional neural networks (CNNs), also on the CIFAR-10 and MNIST datasets. 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.