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Partial 3D object retrieval using local binary QUICCI descriptors and dissimilarity tree indexing
- Source :
- Computers & graphics
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.<br />Comment: 19 pages, 17 figures, to be published in Computers & Graphics
- Subjects :
- FOS: Computer and information sciences
business.industry
Intersection (set theory)
Computer science
Computer Vision and Pattern Recognition (cs.CV)
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Search engine indexing
Computer Science - Computer Vision and Pattern Recognition
General Engineering
Binary number
Pattern recognition
Object (computer science)
Computer Graphics and Computer-Aided Design
Pipeline (software)
Image (mathematics)
Human-Computer Interaction
Tree (data structure)
Index (publishing)
Artificial intelligence
business
Subjects
Details
- ISSN :
- 00978493
- Volume :
- 100
- Database :
- OpenAIRE
- Journal :
- Computers & Graphics
- Accession number :
- edsair.doi.dedup.....eb0456b20608dcde5cd882ef499be98a