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.

This paper presents a novel method for efficient image retrieval, based on a simple and effective hashing of CNN features and the use of an indexing structure based on Bloom filters. These filters are used as gatekeepers for the database of image features, allowing to avoid to perform a query if the query features are not stored in the database and speeding up the query process, without affecting retrieval performance. Thanks to the limited memory requirements the system is suitable for mobile applications and distributed databases, associating each filter to a distributed portion of the database (database shard), addressing large scale archives and allowing query parallelization. Experimental validation has been performed on three standard image retrieval datasets, outperforming state-of-the-art hashing methods in terms of precision, while the proposed indexing method obtains a 2x speedup.