Supervised discrete discriminant hashing for image retrieval

Hashing has been recognized as an efficient representation learning method to effectively handle big data due to its low computational complexity and memory cost. Most of the existing hashing methods focus on learning the low-dimensional vectorized binary features based on the high-dimensional raw vectorized features. However, studies on how to obtain preferable binary codes from the original 2D image features for retrieval is very limited. This paper proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image features which utilizes bilinear projections to binarize the image matrix features such that the intrinsic characteristics in the 2D image space are preserved in the learned binary codes. Meanwhile, the bilinear projection approximation and vectorization binary codes regression are seamlessly integrated together to formulate the final robust learning framework. Furthermore, a discrete optimization strategy is developed to alternatively update each variable for obtaining the high-quality binary codes. In addition, two 2D image features, traditional SURF-based FVLAD feature and CNN-based AlexConv5 feature are designed for further improving the performance of the proposed BSDH method. Results of extensive experiments conducted on four benchmark datasets show that the proposed BSDH method almost outperforms all competing hashing methods with different input features by different evaluation protocols.

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“…• calculate W by equation (24). • compute U by equation (26). • learn B bit by bit using DCC descent method by equation (38).…”

Section: Optimization Algorithmmentioning

confidence: 99%

Bilinear Supervised Hashing Based on 2D Image Features

Ding

Wong

Lai

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…Image perceptual hashing is usually called image digital fingerprint or image digital digest, which can map images into a group of hash sequences, which greatly reduces the storage of digital images and brings great convenience to the management and maintenance of images. It has become a research hotspot in the field of multimedia signal processing and security [9][10]. Perceptual feature extraction is the core of perceptual hashing algorithm.…”

Section: Image Perceptual Hashingmentioning

confidence: 99%

A Difference Perceptual Hashing Algorithm for Medical Volume Data Against Local Nonlinear Geometric Attacks

Han¹

2020

IJIRIS

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Traditional image hashing methods can often only resist traditional global geometric attacks and cannot resist local nonlinear geometric attacks. This has a huge impact on the protection, identification and authentication of three-dimensional medical volume data. In response to these problems, a new difference perceptual hashing algorithm for medical volume data is proposed in this paper. It uses the difference between adjacent elements in each column of the volume data feature matrix to generate a hashing sequence. Experiments show that it has a good ability to resist local nonlinear geometric attacks.

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“…A hash function F h , is a transformation that takes an arbitrary size input m, and returns with a string of a fixed size, which is called the hash value h (where h = F h (m)) [6].…”

Section: A Hash Functionmentioning

confidence: 99%

BHA-160: Constructional Design of Hash Function based on NP-hard Problem

Shahrani¹

2019

IJACSA

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Secure hash function is used to protect the integrity of the message transferred on the unsecured network. Changes on the bits of the sender's message are recognized by the message digest produced by the hash function. Hash function is mainly concerned with data integrity, where the data receiver needs to verify whether the message has been altered by eavesdropping by checking the hash value appended with the message. To achieve this purpose, we have to use a secure hash function that is able to calculate the hash value of any message. In this paper, we introduce an alternative hash function based on NP-hard problem. The chosen NP-hard problem is known as Braid Conjugacy problem. This problem has proved to be secure against cryptanalysis attacks.

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Supervised discrete discriminant hashing for image retrieval

Cited by 38 publications

References 14 publications

Bilinear Supervised Hashing Based on 2D Image Features

Bilinear Supervised Hashing Based on 2D Image Features

A Difference Perceptual Hashing Algorithm for Medical Volume Data Against Local Nonlinear Geometric Attacks

BHA-160: Constructional Design of Hash Function based on NP-hard Problem

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