Lexicographical framework for image hashing with implementation based on DCT and NMF
Lexicographical framework for image hashing with implementation based on DCT and NMF
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DOI：
10.1007/s11042-009-0437-y
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发表时间：
2011-04
影响因子：
3.6
通讯作者：
Zhenjun Tang;Shuozhong Wang;Xinpeng Zhang;Weimin Wei;Yan Zhao
中科院分区：
文献类型：
--
作者：
Zhenjun Tang;Shuozhong Wang;Xinpeng Zhang;Weimin Wei;Yan Zhao
图像散列是一种基于内容的图像压缩表示，用于图像复制检测、数字水印和图像认证等应用。本文提出了一个字典结构的框架来生成图像散列。该系统由两部分组成：字典的构建和维护，以及哈希生成。字典是一个大的集合的特征向量称为词，代表各种图像块的特点。它由多个子字典组成，每个子字典包含许多特征，其数量随着训练图像数量的增加而增加。字典用于提供基本构建块，即单词，以形成哈希。在散列生成中，输入图像的块由与子字典相关联的特征表示。这是通过使用相似性度量来找到每个子字典的选择性特征中最相似的特征来实现的。相应的特征被组合以产生中间散列。通过对中间散列进行编码来获得最终散列。在此框架下，我们利用离散余弦变换（DCT）和非负矩阵分解（NMF）实现了一个哈希算法。实验结果表明，该方案是抵抗正常的内容保持操作，并具有很低的碰撞概率。
Image hash is a content-based compact representation of an image for applications such as image copy detection, digital watermarking, and image authentication. This paper proposes a lexicographical-structured framework to generate image hashes. The system consists of two parts: dictionary construction and maintenance, and hash generation. The dictionary is a large collection of feature vectors called words, representing characteristics of various image blocks. It is composed of a number of sub-dictionaries, and each sub-dictionary contains many features, the number of which grows as the number of training images increase. The dictionary is used to provide basic building blocks, namely, the words, to form the hash. In the hash generation, blocks of the input image are represented by features associated to the sub-dictionaries. This is achieved by using a similarity metric to find the most similar feature among the selective features of each sub-dictionary. The corresponding features are combined to produce an intermediate hash. The final hash is obtained by encoding the intermediate hash. Under the proposed framework, we have implemented a hashing scheme using discrete cosine transform (DCT) and non-negative matrix factorization (NMF). Experimental results show that the proposed scheme is resistant to normal content-preserving manipulations, and has a very low collision probability.