Robust image hashing via visual attention model and ring partition
Robustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2019-07-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2019305?viewType=HTML |
| _version_ | 1828793035894292480 |
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| author | Zhenjun Tang Yongzheng Yu Hanyun Zhang Mengzhu Yu Chunqiang Yu Xianquan Zhang |
| author_facet | Zhenjun Tang Yongzheng Yu Hanyun Zhang Mengzhu Yu Chunqiang Yu Xianquan Zhang |
| author_sort | Zhenjun Tang |
| collection | DOAJ |
| description | Robustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation robustness. In the proposed image hashing, a visual attention model called PFT (Phase spectrum of Fourier Transform) model is used to detect saliency map of preprocessed image. The LL sub-band of saliency map is then divided into concentric circles invariant to rotation by ring partition, and the means and variances of DWT coefficients on concentric circles are taken as image features. Next, these features are encrypted by a chaotic map and the Euclidean distances between normalized encrypted features are finally exploited to generate hash. Similarity between hashes is measured by L1 norm. Many experimental tests show that our image hashing is robust to digital operations including rotation and reaches good discrimination. Comparisons demonstrate that classification performance of our image hashing outperforms those of some well-known hashing algorithms in terms of receiver operating characteristics curves. Simulation of image copy detection is carried out on an open image database called UCID and the result validates effectiveness of our hashing. |
| first_indexed | 2024-12-12T03:19:13Z |
| format | Article |
| id | doaj.art-80010fdd57614faea9c5405cf25b903e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-12T03:19:13Z |
| publishDate | 2019-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-80010fdd57614faea9c5405cf25b903e2022-12-22T00:40:12ZengAIMS PressMathematical Biosciences and Engineering1551-00182019-07-011656103612010.3934/mbe.2019305Robust image hashing via visual attention model and ring partitionZhenjun Tang0Yongzheng Yu 1Hanyun Zhang2Mengzhu Yu3Chunqiang Yu 4Xianquan Zhang5Guangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaGuangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaGuangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaGuangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaGuangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaGuangxi Key Lab of Multi-source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, ChinaRobustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation robustness. In the proposed image hashing, a visual attention model called PFT (Phase spectrum of Fourier Transform) model is used to detect saliency map of preprocessed image. The LL sub-band of saliency map is then divided into concentric circles invariant to rotation by ring partition, and the means and variances of DWT coefficients on concentric circles are taken as image features. Next, these features are encrypted by a chaotic map and the Euclidean distances between normalized encrypted features are finally exploited to generate hash. Similarity between hashes is measured by L1 norm. Many experimental tests show that our image hashing is robust to digital operations including rotation and reaches good discrimination. Comparisons demonstrate that classification performance of our image hashing outperforms those of some well-known hashing algorithms in terms of receiver operating characteristics curves. Simulation of image copy detection is carried out on an open image database called UCID and the result validates effectiveness of our hashing.https://www.aimspress.com/article/doi/10.3934/mbe.2019305?viewType=HTMLimage hashingvisual attention modelring partitionsaliency mapimage copy detection |
| spellingShingle | Zhenjun Tang Yongzheng Yu Hanyun Zhang Mengzhu Yu Chunqiang Yu Xianquan Zhang Robust image hashing via visual attention model and ring partition Mathematical Biosciences and Engineering image hashing visual attention model ring partition saliency map image copy detection |
| title | Robust image hashing via visual attention model and ring partition |
| title_full | Robust image hashing via visual attention model and ring partition |
| title_fullStr | Robust image hashing via visual attention model and ring partition |
| title_full_unstemmed | Robust image hashing via visual attention model and ring partition |
| title_short | Robust image hashing via visual attention model and ring partition |
| title_sort | robust image hashing via visual attention model and ring partition |
| topic | image hashing visual attention model ring partition saliency map image copy detection |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2019305?viewType=HTML |
| work_keys_str_mv | AT zhenjuntang robustimagehashingviavisualattentionmodelandringpartition AT yongzhengyu robustimagehashingviavisualattentionmodelandringpartition AT hanyunzhang robustimagehashingviavisualattentionmodelandringpartition AT mengzhuyu robustimagehashingviavisualattentionmodelandringpartition AT chunqiangyu robustimagehashingviavisualattentionmodelandringpartition AT xianquanzhang robustimagehashingviavisualattentionmodelandringpartition |