Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

Secure and robust image hashing scheme

Abstract: With the development of Internet,the issue of multimedia copyright protection becomes urgent day and day,making the image hashing for image authentication obtaining extensive research and application.This paper proposed a novel image hashing scheme based on V-system.It first modulated the image pixels to get a intensity-transformed version,then extracted coefficients after V transform to generate the hash value.The experimental results demonstrate that the scheme has a good performance against attacks.

Kernelised supervised context hashing

Abstract: 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.

A New Bilinear Supervised Neighborhood Discrete Discriminant Hashing

Abstract: Feature extraction is an important part of perceptual hashing. How to compress the robust features of images into hash codes has become a hot research topic. Converting a two-dimensional image into a one-dimensional descriptor requires a higher computational cost and is not optimal. In order to maintain the internal feature structure of the original two-dimensional image, a new Bilinear Supervised Neighborhood Discrete Discriminant Hashing (BNDDH) algorithm is proposed in this paper. Firstly, the algorithm constructs two new neighborhood graphs to maintain the geometric relationship between samples and reduces the quantization loss by directly constraining the hash codes. Secondly, two small rotation matrices are used to realize the bilinear projection of the two-dimensional descriptor. Finally, the experiment verifies the performance of the BNDDH algorithm under different feature types, such as image original pixels and a Convolutional Neural Network (CNN)-based AlexConv5 feature. The experimental results and discussion clearly show that the proposed BNDDH algorithm is better than the existing traditional hashing algorithm and can represent the image more efficiently in this paper.