Efficient BOF Generation and Compression for On-Device Mobile Visual Location Recognition

摘要

Existing mobile visual location recognition (MVLR) applications typically rely on bag-of-features (BOF) representation, which shows superior performance in retrieval accuracy. However, although the BOF framework is promising, it is not compact enough for on-device MVLR. The authors have made two contributions to the design of a BOF-based on-device MVLR system. First, to generate BOF descriptors, they propose a memory-efficient approximate nearest-neighbor search algorithm by combining residual vector quantization (RVQ) and tree-structured RVQ (TSRVQ). Second, they implemented a GPS-based and heading-aware RankBoost algorithm to reduce the dimensionality of the BOF descriptors. The authors evaluate the effectiveness of the proposed algorithms on an HTC mobile phone. Their work applies to on-device MVLR in city-scale workspaces.