LAUSR

The convolutional neural network (CNN) features can give good description of image content, which usually represent an image with a single feature vector. Although CNN features are more compact than local descriptors, they still cannot efficiently deal with large-scale retrieval due to the linearly incremental cost of computation and storage. To address this issue, we build a simple but effective indexing framework on inverted table, which significantly decreases both search time and memory usage. First, several strategies are fully investigated to adapt inverted table to CNN features for compensating for quantization error. We use multiple assignment for the query and database images to increase the probability that relevant images are assigned to the same visual word obtained via clustering. Embedding codes are also introduced to improve retrieval accuracy by removing false matches. Second, a novel indexing framework that combines inverted table and hashing codes is proposed. This framework is faster than the reformed inverted tables with the introduced strategies. Experiment on several benchmark datasets demonstrates that our method yields faster retrieval speed compared to brute-force search. We also provide fair comparison between popular CNN features.