Open AccessKernelised supervised context hashing
Author(s) -
Li YunQiang,
Zha YuFei,
Qin Bing,
Tian Jun,
Liu Chang
Publication year - 2016
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2015.0848
Subject(s) - dynamic perfect hashing , universal hashing , hash function , k independent hashing , binary code , computer science , hash table , locality sensitive hashing , metric (unit) , double hashing , theoretical computer science , context (archaeology) , hamming distance , linear hashing , feature hashing , hamming space , pattern recognition (psychology) , mnist database , algorithm , binary number , artificial intelligence , mathematics , hamming code , block code , decoding methods , deep learning , arithmetic , paleontology , operations management , computer security , economics , biology
Most existing supervised hashing methods learn the affinity‐preserving binary codes to represent the high‐dimensional data. However, each hashing code is assumed as independent and irrelevant with other codes. In practice, the authors find that there exists context association among hashing bits. This study proposes a novel hashing method dubbed kernelised supervised context hashing, which considers the hashing codes interrelation to reduce the quantisation. In this work, the kernel formulation is employed to tackle the high‐dimensional data which is mostly linear inseparable first; and then different distributions are utilised to describe the binary codes context; finally, the hashing codes can be approximated by gradient descent method iteratively. Therefore, the correlation between the hash codes is integrated to redefine the metric measurement (i.e. Hamming affinity) to preserve the data similarity in the raw space. The authors evaluate the proposed method on three image benchmarks CIFAR‐10, MNIST and NUS‐WIDE for image retrieval, and experimental results show that it achieves better performance than several other state‐of‐the‐art methods.