  A binary descriptor indexing scheme based on Hamming distance called the
Hamming tree for local shape queries is presented. A new binary clutter
resistant descriptor named Quick Intersection Count Change Image (QUICCI) is
also introduced. This local shape descriptor is extremely small and fast to
compare. Additionally, a novel distance function called Weighted Hamming
applicable to QUICCI images is proposed for retrieval applications. The
effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million
QUICCI images derived from the SHREC2017 dataset, while the clutter resistance
of QUICCI is shown using the clutterbox experiment.
