histogram-based image hashing for searching content-preserving copies
文献类型：期刊论文
| 作者 | Xiang Shijun ; Kim Hyoung Joong |
| 刊名 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| 出版日期 | 2011 |
| 卷号 | 6730 LNCS页码:83-108 |
| 关键词 | Additive noise Deformation Graphic methods Hash functions |
| ISSN号 | 0302-9743 |
| 中文摘要 | Image hashing as a compact abstract can be used for content search. Towards this end, a desired image hashing function should be resistant to those content-preserving manipulations (including additive-noise like processing and geometric deformation operations). Most countermeasures proposed in the literature usually focus on the problem of additive noises and global affine transform operations, but few are resistant to recently reported random bending attacks (RBAs). In this paper, we address an efficient and effective image hashing algorithm by using the resistance of two statistical features (image histogram in shape and mean value) for those challenging geometric deformations. Since the features are extracted from Gaussian-filtered images, the hash is also robust to common additive noise-like operations (e.g., lossy compression, low-pass filtering). The hash uniqueness is satisfactory for different sources of images. With a large number of real-world images, we construct a hash-based image search system to show that the hash function can be used for searching content-preserving copies from the same source. © 2011 Springer-Verlag Berlin Heidelberg. |
| 英文摘要 | Image hashing as a compact abstract can be used for content search. Towards this end, a desired image hashing function should be resistant to those content-preserving manipulations (including additive-noise like processing and geometric deformation operations). Most countermeasures proposed in the literature usually focus on the problem of additive noises and global affine transform operations, but few are resistant to recently reported random bending attacks (RBAs). In this paper, we address an efficient and effective image hashing algorithm by using the resistance of two statistical features (image histogram in shape and mean value) for those challenging geometric deformations. Since the features are extracted from Gaussian-filtered images, the hash is also robust to common additive noise-like operations (e.g., lossy compression, low-pass filtering). The hash uniqueness is satisfactory for different sources of images. With a large number of real-world images, we construct a hash-based image search system to show that the hash function can be used for searching content-preserving copies from the same source. © 2011 Springer-Verlag Berlin Heidelberg. |
| 收录类别 | EI |
| 语种 | 英语 |
| 公开日期 | 2013-10-08 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16154]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Xiang Shijun,Kim Hyoung Joong. histogram-based image hashing for searching content-preserving copies[J]. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2011,6730 LNCS:83-108. |
| APA | Xiang Shijun,&Kim Hyoung Joong.(2011).histogram-based image hashing for searching content-preserving copies.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),6730 LNCS,83-108. |
| MLA | Xiang Shijun,et al."histogram-based image hashing for searching content-preserving copies".Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6730 LNCS(2011):83-108. |
入库方式： OAI收割
来源：软件研究所
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