Kernelised supervised context hashing
Yunqiang Li, Yufei Zha, Bing Qin, Jun Tian, Chang Liu · IET Image Processing · 2016
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.