在OpenCV中匹配相似的图像 [英] Matching similar images in OpenCV

问题描述

我有两组图像{H}和{L}。 {H}由512x512图像组成。 {L}由{H}中的所有图像组成，但缩小为32x32-128x128，并且具有来自有损压缩的压缩工件。

I have two sets of images, {H} and {L}. {H} consists of 512x512 images. {L} consists of all of the images in {H}, but scaled down to 32x32-128x128 and with compression artifacts from lossy compression.

最佳方式是什么？使用OpenCV将{H}中的图像与{L}中最接近的匹配相匹配？

What would be the best way of matching images in {H} to their closest match in {L} using OpenCV?

推荐答案

另一种，尽管可能慢得多是按压缩聚类（Arxviv.org，PDF）并且可能使用JPEG系数作为要比较的模型数据，而不是其他一些压缩的未压缩图像数据压缩方法。另请参阅与Google学术搜索的第一篇论文相关的文章。

Another, although maybe much slower approach is to do Clustering by Compression (Arxviv.org, PDF) and maybe use the JPEG coefficients as the model data to be compared instead of the uncompressed image data compressed by some other method of compression. Also see articles related to the first paper from Google Scholar.

通过压缩进行聚类主要是指使用文件Y中的（统计）模型压缩文件X并进行比较使用它自己的模型数据来压缩X的大小。

Clustering by compression basically means compressing a file X using the (statistical) model from file Y and compare to the size to just compressing X with it’s own model’s data.

以下是关于使用不同的统计模型进行压缩。 JPEG压缩使用霍夫曼编码或算术编码来压缩DC系数表。

Here is some background about the idea of using different statistical models for compression. JPEG compression uses Huffman coding or Arithmetic coding to compress the DC coefficient tables.

另一种选择，如果较小的图像不仅仅是下采样和/或裁剪版本，可能要快得多，就是使用Wajih建议的SIFT或SURF算法。

Yet another option, which may be much faster if the smaller images are not just downsampled and/or cropped versions, is to use the SIFT or SURF algorithms as suggested by Wajih.

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