Open AccessRobust image hashing with visual attention model and invariant moments
Author(s) -
Tang Zhenjun,
Zhang Hanyun,
Pun ChiMan,
Yu Mengzhu,
Yu Chunqiang,
Zhang Xianquan
Publication year - 2020
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.2019.1157
Subject(s) - discriminative model , artificial intelligence , feature hashing , robustness (evolution) , computer science , pattern recognition (psychology) , hash function , invariant (physics) , locality sensitive hashing , image retrieval , computer vision , hash table , mathematics , image (mathematics) , double hashing , biochemistry , chemistry , gene , computer security , mathematical physics
Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub‐band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.