重复图像检测算法？

编程入门行业动态更新时间:2024-10-17 23:21:43

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我想建立一个数据库系统，它们的存储紧凑的签名，并随后将其与一个查询图像这可能是一个调整大小的图像，裁剪，增白，旋转或存储之一的翻转版本。请注意，我说的不是图像相似的算法，而是严格地约重复检测。这将使事情变得简单许多。该系统也不会介意两个图像对他们的大象，它只会以检测两个图像其实都是相同的图像是非常重要的。

I am thinking about creating a database system for images where they are stored with compact signatures and then matched against a "query image" that could be a resized, cropped, brightened, rotated or a flipped version of the stored one. Note that I am not talking about image similarity algorithms but rather strictly about duplicate detection. This would make things a lot simpler. The system wouldn't care if two images have an elephant on them, it would only be important to detect if the two images are in fact the same image.

直方图比较根本不会对裁剪查询图像的工作。唯一可行的办法去，我看到的是形状/边缘检测。图像将首先以某种离散化，则每个像素被转换为一个8级灰度的例子。离散化的图像将包含在相同颜色的广大地区，这将有助于表明形状。这些形状，然后可以用系数来描述它们的相对位置可以被记住。小型签名会产生出来的。这个过程将进行在每个图像被存储和在每个查询图像时的比较必须执行。这听起来像一个有效的和可实现的算法？为了说明这个道理：

Histogram comparisons simply won't work for cropped query images. The only viable way to go I see is shape/edge detection. Images would first be somehow discretized, every pixel being converted to an 8-level grayscale for example. The discretized image will contain vast regions in the same colour which would help indicate shapes. These shapes then could be described with coefficients and their relative position could be remembered. Compact signatures would be produced out of that. This process will be carried out over each image being stored and over each query image when a comparison has to be performed. Does that sound like an efficient and realisable algorithm? To illustrate this idea:

删除死ImageShack链接的

我知道这是一个不成熟的研究领域，我已阅读维基百科关于这个问题，我想请你提出关于这种算法你的想法。

I know this is an immature research area, I have read Wikipedia on the subject and I would ask you to propose your ideas about such an algorithm.

推荐答案

SURF应该做的工作。

SURF should do its job.

en.wikipedia/wiki/SURF

这是一个快速稳健，这是不变的旋转和缩放，并在blure和对比度/闪电（但不那么强烈）。还有比如自动全景拼接功能。

It is fast an robust, it is invariant on rotations and scaling and also on blure and contrast/lightning (but not so strongly). There is example of automatic panorama stitching.