Unsupervised binary hashing method using locality preservation and quantisation error minimisation

2015 ◽  
Vol 51 (3) ◽  
pp. 255-257 ◽  
Author(s):  
Jin‐Bum Kim ◽  
Rae‐Hong Park
Keyword(s):  
Locality Preservation ◽  
Quantisation Error

Zero-Shot Classification for Remote Sensing Scenes Based on Locality Preservation

2019 ◽  
Vol 39 (7) ◽  
pp. 0728001
Author(s):  
吴晨 Chen Wu ◽  
王宏伟 Hongwei Wang ◽  
王志强 Zhiqiang Wang ◽  
袁昱纬 Yuwei Yuan ◽  
刘宇 Yu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shot Classification ◽  
Locality Preservation

scholarly journals A Parallel N-Dimensional Space-Filling Curve Library and Its Application in Massive Point Cloud Management

2018 ◽  
Vol 7 (8) ◽  
pp. 327 ◽  
Author(s):  
Xuefeng Guan ◽  
Peter van Oosterom ◽  
Bo Cheng
Keyword(s):  
Point Cloud ◽  
Parallel Implementation ◽  
Dimensional Space ◽  
Space Filling ◽  
Oracle Database ◽  
Space Filling Curve ◽  
Object Oriented Design ◽  
Massive Point ◽  
Locality Preservation ◽  
Cloud Management

Because of their locality preservation properties, Space-Filling Curves (SFC) have been widely used in massive point dataset management. However, the completeness, universality, and scalability of current SFC implementations are still not well resolved. To address this problem, a generic n-dimensional (nD) SFC library is proposed and validated in massive multiscale nD points management. The library supports two well-known types of SFCs (Morton and Hilbert) with an object-oriented design, and provides common interfaces for encoding, decoding, and nD box query. Parallel implementation permits effective exploitation of underlying multicore resources. During massive point cloud management, all xyz points are attached an additional random level of detail (LOD) value l. A unique 4D SFC key is generated from each xyzl with this library, and then only the keys are stored as flat records in an Oracle Index Organized Table (IOT). The key-only schema benefits both data compression and multiscale clustering. Experiments show that the proposed nD SFC library provides complete functions and robust scalability for massive points management. When loading 23 billion Light Detection and Ranging (LiDAR) points into an Oracle database, the parallel mode takes about 10 h and the loading speed is estimated four times faster than sequential loading. Furthermore, 4D queries using the Hilbert keys take about 1~5 s and scale well with the dataset size.

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