Residual enhanced visual vector as a compact signature for mobile visual search

Many mobile visual search (MVS) systems transmit query data from a mobile device to a remote server and search a database hosted on the server. In this paper, we present a new architecture for searching a large database directly on a mobile device, which can provide numerous benefits for network-independent, low-latency, and privacy-protected image retrieval. A key challenge for on-device retrieval is storing a large database in the limited RAM of a mobile device. To address this challenge, we develop a new compact, discriminative image signature called the Residual Enhanced Visual Vector (REVV) that is optimized for sets of local features which are fast to extract on mobile devices. REVV outperforms existing compact database constructions in the MVS setting and attains similar retrieval accuracy in large-scale retrieval as a Vocabulary Tree that uses 25×25× more memory. We have utilized REVV to design and construct a mobile augmented reality system for accurate, large-scale landmark recognition. Fast on-device search with REVV enables our system to achieve latencies around 1 s per query regardless of external network conditions. The compactness of REVV allows it to also function well as a low-bitrate signature that can be transmitted to or from a remote server for an efficient expansion of the local database search when required.

► We have developed a compact global image signature for large-scale mobile visual search.
► Our signature enables a large database to be stored on a mobile device with limited memory.
► The same compact signature can be used to perform very low-bitrate visual queries.
► Large-scale retrieval experiments show our signature is both compact and discriminative.