Wavelet-Based Multiresolution Features for Detecting Duplications in Images
Wavelet-Based Multiresolution Features for Detecting Duplications in Images
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DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Md. Khayrul Bashar;K. Noda;N. Ohnishi;H. Kudo;Tetsuya Matsumoto;Y. Takeuchi
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文献类型：
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作者：
Md. Khayrul Bashar;K. Noda;N. Ohnishi;H. Kudo;Tetsuya Matsumoto;Y. Takeuchi
复制图像区域是使用Adobe Photoshop等典型软件处理原始图像的常见方法。在这项研究中，我们提出了一个基于小波的特征表示方案，用于检测图像中的重复区域。该技术的工作原理是首先将多分辨率小波分解应用于小的固定大小的图像块。然后将归一化的小波系数以从低到高的频率的顺序堆叠成向量。这种表示对于块匹配来说是鲁棒的。然后通过对所有图像块进行字典式排序并将阈值应用于块坐标的偏移的期望频率来检测重复区域。还提出了一种半自动技术，检测准确的重复区域的数量。与基于线性PCA的表示相比，具有重复区域的一组自然图像的初始实验显示出令人印象深刻的结果。
Duplication of image regions is a common method for manipulating original images using typical software like Adobe Photoshop. In this study, we propose a wavelet based feature representation scheme for detecting duplicated regions in images. This technique works by first applying multi-resolution wavelet decomposition to small fixed-sized image blocks. Normalized wavelet coefficients are then stacked into a vector in an order from lower to higher frequencies. This kind of representation appears robust to block matching. Duplicated regions are then detected by lexicographically sorting all of the image blocks and applying threshold to the desired frequency of the offsets of the blockcoordinates. A semi-automatic technique that detects accurate number of duplicated regions is also proposed. Initial experiments with a set of natural images having duplicated regions show impressive results compared to linear PCA based representation.