blocks|key|2522007|text|这是一种你可以测试两个图像之间是否有相似性的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2522008|2522009|blockquote|2522010|2522011|2522012|将两个图像分成4*4或8*8个块|ordered-list-item|2522013|从两个相应块的差值中提取平方误差并对照阈值检查|2522014|如果小于阈值，则它们是相似图像|2522015|否则不同<|2522016|>G213>|2522017|另一种方法是：|2522018|正如我们所知，两个相似的帧可能会有一些翻译：|2522019|使用两幅图像的互相关，越高，图像的相关性越强。|2522020|entityMap^0|0|0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|10|8|@]|9|@]|A|$]]|$1|C|3|-4|5|D|7|11|8|@]|9|@]|A|$]]|$1|E|3|-4|5|6|7|12|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|13|8|@]|9|@]|A|$]]|$1|G|3|H|5|I|7|14|8|@]|9|@]|A|$]]|$1|J|3|K|5|I|7|15|8|@]|9|@]|A|$]]|$1|L|3|M|5|I|7|16|8|@]|9|@]|A|$]]|$1|N|3|O|5|I|7|17|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|18|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|19|8|@]|9|@]|A|$]]|$1|T|3|U|5|6|7|1A|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|1B|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]]|Y|$]]

Here is a way you can test whether there is similarity between two image : -

<blockquote>
 <ol>
 <li>Divide two image into 4*4 or 8*8 blocks</li>
 <li>Take squared error from difference of two corresponding blocks from both images</li>
 <li>Accumulate the errors and check it against the threshold </li>
 <li>if less than threshold then they are similar images</li>
 <li>else different</li>
 </ol>
</blockquote>

Another way to do it : - 

As we know two similar frames can have bit of translation in them : -

Use Cross-Correlation of the two images, higher it is the more related the images are.

blocks|key|3814581|text|您是否尝试过通过计算直方图并将它们相互比较(例如，使用EMD+)来获得任何结果？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3814582|视频中的大变化将显示为直方图中的大变化|3814583|我记得读过一篇论文，作者会通过检测直方图中的大跳跃来检测场景的剪切。|3814584|编辑:这不是我想要的，但看起来不错。This+paper介绍了几种技术及其相对性能，John+S.Boreczky和Lawrence+A.Rowe，“视频镜头边界检测技术的比较”，图像和视频数据库的存储和检索，1996年。|offset|length|3814585|entityMap|0|LINK|mutability|MUTABLE|url|http://www.google.fr/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&sqi=2&ved=0CDUQFjAA&url=http://www.researchgate.net/publication/2857629_Comparison_of_Video_Shot_Boundary_Detection_Techniques/file/d912f511294c5669e8.pdf&ei=aKXWUv_pFcWzhAfgvYHoCQ&usg=AFQjCNFZHRVav9CxC1-u8GJUerfWRaT__w&sig2=Aa3J14tv761hTy3bWG53gg&bvm=bv.59378465,d.d2k^0|0|0|0|I|A|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|S|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@$H|V|I|W|1|X]]|A|$]]|$1|J|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]]]

Have you tried getting any result by computing the histograms and comparing them to one another (with EMD for instance) ?

A big change in the video would show up as a big change in the histogram would 

I recall reading a paper where the authors would detect scenes cut by detecting a big jump in the histogram.

EDIT: This is not what I had in mind, but it looks good. <a href="http://www.google.fr/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=1&amp;sqi=2&amp;ved=0CDUQFjAA&amp;url=http://www.researchgate.net/publication/2857629_Comparison_of_Video_Shot_Boundary_Detection_Techniques/file/d912f511294c5669e8.pdf&amp;ei=aKXWUv_pFcWzhAfgvYHoCQ&amp;usg=AFQjCNFZHRVav9CxC1-u8GJUerfWRaT__w&amp;sig2=Aa3J14tv761hTy3bWG53gg&amp;bvm=bv.59378465,d.d2k" rel="nofollow">This paper</a> presents several techniques and their relative performance
John S. Boreczky and Lawrence A. Rowe, « Comparison of video shot boundary detection techniques », Storage and Retrieval for Image and Video Databases (SPIE), 1996.

blocks|key|3814613|text|这是一个使用MATLAB的计算机视觉系统工具箱进行基于边缘检测的场景变化检测的example。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3814614|entityMap|0|LINK|mutability|MUTABLE|url|http://www.mathworks.com/help/vision/examples/scene-change-detection.html^0|13|7|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Here is an <a href="http://www.mathworks.com/help/vision/examples/scene-change-detection.html" rel="nofollow">example</a> for scene change detection based on edge detection using MATLAB's Computer Vision System toolbox.

blocks|key|33937|text|您可以尝试以下两种方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|33938|33939|直接计算Mat数据的差值(使用OpenCV编写代码)：|ordered-list-item|33940|33941|cv::Mat+diff；cv::absdiff(imgA，imgB，diff)；|33942|计算两幅图像的特征向量之差。例如，您可以首先计算每个图像的RGB/HSV颜色直方图(如果每个通道使用8个柱状图，则为24d向量)，然后计算这两个直方图向量的相关性。|unordered-list-item|33943|33944|entityMap^0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|P|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|Q|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|T|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

You can try the following two approaches:

<ol>
<li>Compute difference on the Mat data directly (code using OpenCV):

<pre><code>cv::Mat diff;
cv::absdiff(imgA, imgB, diff);
</code></pre></li>
<li>Compute difference on the feature vectors of two images. For example, you can first compute RGB/HSV color histogram (24d vector if use 8-bins for each channel) for each image and then compute correlation of these two histogram vectors.</li>
</ol>

blocks|key|33981|text|计算相邻帧的RGB直方图的卡方距离是一种稳健的方法。您可以在here中查看此方法的实现和用法。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|33982|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/yasinyildirim/ShotDetection^0|U|4|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Computing Chi-Square distance of RGB histograms of adjacent frames is one of the robust methods. You can see the implementation and usage of this method in <a href="https://github.com/yasinyildirim/ShotDetection" rel="nofollow noreferrer">here</a>.

I have been looking for algorithms related to video change detection.

Basically it will see if there is any visual difference in the current frame wrt to previous frame or the first frame of the video and show use the difference.

Can you please suggest an algorithm?

My implementation: I did read video frame by frame and converted to gray scale subtracted each frame with first frame and played the video(current frame-first frame)

Video change detection

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我一直在寻找与视频变化检测相关的算法。基本上，它将查看当前帧wrt中是否存在与视频的前一帧或第一帧的视觉差异，并使用该差异进行显示。你能推荐一个算法吗？我的实现:我逐帧读取视频并转换成灰度，用第一帧减去每一帧，然后播放视频(当前帧-第一帧)。

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