比较图像相似性的简单快速方法 [英] Simple and fast method to compare images for similarity
问题描述
我需要一种简单快速的方法来比较两个图像的相似性.IE.如果它们包含完全相同的内容，但背景可能略有不同，并且可能会移动/调整几个像素的大小，我想获得较高的值.
(更具体地说，如果这很重要:一张图片是图标，另一张图片是屏幕截图的子区域，我想知道该子区域是否正是图标.)
我手头有 OpenCV，但我仍然不习惯它.
到目前为止我想到的一种可能性:将两张图片分成 10x10 个单元格，并针对这 100 个单元格中的每一个，比较颜色直方图.然后我可以设置一些虚构的阈值，如果我得到的值高于该阈值，我认为它们是相似的.
我还没有尝试过它的效果如何，但我想它已经足够好了.图像已经非常相似(在我的用例中)，因此我可以使用相当高的阈值.
我想有很多其他可能的解决方案可以或多或少地起作用(因为任务本身非常简单，因为我只想检测相似性，如果它们真的非常相似).你有什么建议?
<小时>关于从图像中获取签名/指纹/哈希有几个非常相关/相似的问题:
- OpenCV/SURF如何从描述符中生成图像哈希/指纹/签名?
- 用于比较多幅图像相似度的图像指纹
- 近似重复图像检测
- OpenCV:指纹图像并与数据库进行比较.莉>
- 更多、更多，更多、更多, 更多, 更多、更多
此外，我偶然发现了这些具有获取指纹功能的实现:
- pHash
- imgSeek (GitHub 存储库) (GPL) 基于论文 快速多分辨率图像查询
- 图像匹配.与我正在寻找的非常相似.与 pHash 类似，基于 任何类型图像的图像签名，Goldberg 等人.使用 Python 和 Elasticsearch.
- iqdb
- ImageHash.支持 pHash.
- 图像重复数据删除器 (imagededup).支持CNN、PHash、DHash、WHash、AHash.
关于感知图像哈希的一些讨论:这里
<小时>有点题外话:有很多方法可以创建音频指纹.MusicBrainz 是一种提供基于指纹的歌曲查找的网络服务，具有 他们的 wiki 中有很好的概述.他们现在使用 AcoustID.这是用于查找精确(或大部分精确)匹配.要查找相似的匹配项(或者如果您只有一些片段或高噪声)，请查看 Echoprint.一个相关的 SO 问题是这里.所以这似乎解决了音频.所有这些解决方案都非常有效.
关于模糊搜索的一个更通用的问题是这里.例如.有 locality-sensitive hashing 和 最近邻搜索.
屏幕截图或图标是否可以转换(缩放、旋转、倾斜...)?我脑子里有很多方法可以帮助你:
- 简单欧几里得距离，如@carlosdc 所述(不适用于转换后的图像，您需要一个阈值).
- (标准化)互相关 - 一个简单的指标您可以使用它来比较图像区域.它比简单的欧几里德距离更稳健，但不适用于转换后的图像，您将再次需要一个阈值.
- 直方图比较 - 如果您使用归一化直方图，则此方法运行良好且不受仿射变换的影响.问题是确定正确的阈值.它对颜色变化(亮度、对比度等)也非常敏感.您可以将其与前两者结合起来.
- 显着点/区域检测器 - 例如 MSER(最大稳定极值区域))、SURF 或 SIFT.这些是非常强大的算法，对于您的简单任务来说它们可能太复杂了.好消息是，您不必有一个只有一个图标的精确区域，这些检测器功能强大，可以找到合适的匹配项.本文对这些方法进行了很好的评估:局部不变特征检测器:调查.
其中大部分已经在 OpenCV 中实现 - 例如参见 cvMatchTemplate 方法(使用直方图匹配):http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html.显着点/区域检测器也可用 - 请参阅 OpenCV 特征检测.>
I need a simple and fast way to compare two images for similarity. I.e. I want to get a high value if they contain exactly the same thing but may have some slightly different background and may be moved / resized by a few pixel.
(More concrete, if that matters: The one picture is an icon and the other picture is a subarea of a screenshot and I want to know if that subarea is exactly the icon or not.)
I have OpenCV at hand but I am still not that used to it.
One possibility I thought about so far: Divide both pictures into 10x10 cells and for each of those 100 cells, compare the color histogram. Then I can set some made up threshold value and if the value I get is above that threshold, I assume that they are similar.
I haven't tried it yet how well that works but I guess it would be good enough. The images are already pretty much similar (in my use case), so I can use a pretty high threshold value.
I guess there are dozens of other possible solutions for this which would work more or less (as the task itself is quite simple as I only want to detect similarity if they are really very similar). What would you suggest?
There are a few very related / similar questions about obtaining a signature/fingerprint/hash from an image:
- OpenCV / SURF How to generate a image hash / fingerprint / signature out of the descriptors?
- Image fingerprint to compare similarity of many images
- Near-Duplicate Image Detection
- OpenCV: Fingerprint Image and Compare Against Database.
- more, more, more, more, more, more, more
Also, I stumbled upon these implementations which have such functions to obtain a fingerprint:
- pHash
- imgSeek (GitHub repo) (GPL) based on the paper Fast Multiresolution Image Querying
- image-match. Very similar to what I was searching for. Similar to pHash, based on An image signature for any kind of image, Goldberg et al. Uses Python and Elasticsearch.
- iqdb
- ImageHash. supports pHash.
- Image Deduplicator (imagededup). Supports CNN, PHash, DHash, WHash, AHash.
Some discussions about perceptual image hashes: here
A bit offtopic: There exists many methods to create audio fingerprints. MusicBrainz, a web-service which provides fingerprint-based lookup for songs, has a good overview in their wiki. They are using AcoustID now. This is for finding exact (or mostly exact) matches. For finding similar matches (or if you only have some snippets or high noise), take a look at Echoprint. A related SO question is here. So it seems like this is solved for audio. All these solutions work quite good.
A somewhat more generic question about fuzzy search in general is here. E.g. there is locality-sensitive hashing and nearest neighbor search.
Can the screenshot or icon be transformed (scaled, rotated, skewed ...)? There are quite a few methods on top of my head that could possibly help you:
- Simple euclidean distance as mentioned by @carlosdc (doesn't work with transformed images and you need a threshold).
- (Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.
- Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two.
- Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.
Most of these are already implemented in OpenCV - see for example the cvMatchTemplate method (uses histogram matching): http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html. The salient point/area detectors are also available - see OpenCV Feature Detection.
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