  The convolutional neural network (CNN) features can give a good description
of image content, which usually represent images with unique global vectors.
Although they are compact compared to local descriptors, they still cannot
efficiently deal with large-scale image retrieval due to the cost of the linear
incremental computation and storage. To address this issue, we build a simple
but effective indexing framework based on inverted table, which significantly
decreases both the search time and memory usage. In addition, several
strategies are fully investigated under an indexing framework to adapt it to
CNN features and compensate for quantization errors. First, we use multiple
assignment for the query and database images to increase the probability of
relevant images' co-existing in the same Voronoi cells obtained via the
clustering algorithm. Then, we introduce embedding codes to further improve
precision by removing false matches during a search. We demonstrate that by
using hashing schemes to calculate the embedding codes and by changing the
ranking rule, indexing framework speeds can be greatly improved. Extensive
experiments conducted on several unsupervised and supervised benchmarks support
these results and the superiority of the proposed indexing framework. We also
provide a fair comparison between the popular CNN features.
