An indexing scheme and descriptor for 3D object retrieval based on local shape querying®

被引：9
|
作者：
van Blokland, Bart Iver [1 ]
Theoharis, Theoharis [1 ]
机构：
[1] Norwegian Univ Sci & Technol NTNU, Dept Comp Sci, Trondheim, Norway
来源：
COMPUTERS & GRAPHICS-UK | 2020年 / 92卷
关键词：
Hamming tree; QUICCI; Shape; Descriptor; HISTOGRAMS;
D O I：
10.1016/j.cag.2020.09.001
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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. (C) 2020 The Author(s). Published by Elsevier Ltd.
引用
收藏
页码：55 / 66
页数：12
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