Offline Mobile Instance Retrieval with a Small Memory Footprint

Jayaguru Panda, Michael S. Brown, C.V. Jawahar; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 1257-1264

Existing mobile image instance retrieval applications assume a network-based usage where image features are sent to a server to query an online visual database. In this scenario, there are no restrictions on the size of the visual database. This paper, however, examines how to perform this same task offline, where the entire visual index must reside on the mobile device itself within a small memory footprint. Such solutions have applications on location recognition and product recognition. Mobile instance retrieval requires a significant reduction in the visual index size. To achieve this, we describe a set of strategies that can reduce the visual index up to 60-80 x compared to a standard instance retrieval implementation found on desktops or servers. While our proposed reduction steps affect the overall mean Average Precision (mAP), they are able to maintain a good Precision for the top K results (P K ). We argue that for such offline application, maintaining a good P K is sufficient. The effectiveness of this approach is demonstrated on several standard databases. A working application designed for a remote historical site is also presented. This application is able to reduce an 50,000 image index structure to 25 MBs while providing a precision of 97% for P 10 and 100% for P 1 .