基于内容的图像检索（CBIR）：一袋特征或描述符匹配？ [英] Content Based Image Retrieval (CBIR): Bag of Features or Descriptors Matching?

问题描述

我已经阅读了很多关于最近邻问题的论文，似乎像随机化kd-树或LSH的索引技术已经成功地用于基于内容的图像检索（CBIR），其可以在高维空间。一个真正常见的实验给出一个SIFT查询向量，找到数据集中最相似的SIFT描述符。如果我们用所有检测到的SIFT描述符重复该过程，我们可以找到最相似的图像。

I've read a lot of papers about the Nearest Neighbor problem, and it seems that indexing techniques like randomized kd-trees or LSH has been successfully used for Content Based Image Retrieval (CBIR), which can operate in high dimensional space. One really common experiment is given a SIFT query vector, find the most similar SIFT descriptor in the dataset. If we repeat the process with all the detected SIFT descriptors we can find the most similar image.

然而，另一种流行的方法是使用 Bag of Visual Words ，并将检测到的所有SIFT描述符转换成巨大的稀疏向量，可以使用相同的文本技术（例如倒排索引）。

However, another popular approach is using Bag of Visual Words and convert all the SIFT descriptors detected into an huge sparse vector, which can be indexed with the same text techniques (e.g. inverted index).

我的问题是这两种不同的方法（通过最近邻技术匹配SIFT描述符VS SIFT描述符+反转索引）是非常不同的，我不明白哪一个更好。

My question is: these two different approaches ( matching the SIFT descriptors through Nearest Neighbor technique VS Bag of Features on SIFT descriptors + invert index) are extremely different and I don't understand which one is better.

如果第二种方法更好，什么是最近邻计算机视觉/图像处理？

If the second approach is better, what is the application of Nearest Neighbor in Computer Vision / Image Processing?

推荐答案

哦，男孩，你问一个问题，即使文件不能回答，我想。为了比较，应该采用两种方法的最先进的技术，并比较它们，测量速度，准确性和回忆。

Oh boy, you are asking a question that even the papers can't answer, I think. In order to compare, one should take the state-of-the-art technologies of both approaches and compare them, measure speed, accuracy and recall. The one with the best characteristics is better than the other.

就我个人而言，我没有听说过视觉词袋，我使用过的词袋模型仅在文本相关项目中，不与图像相关。此外，我很肯定，我已经看到很多人使用第一种方法（包括我和我们的研究）。

Personally, I hadn't heard much of the Bag of Visual Words, I had used the bag of words model only in text related projects, not images-relevant ones. Moreover, I am pretty sure I have seen many people use the 1st approach (including me and our research).

这是我得到的最好的，所以如果我是你，我会搜索一个比较这两个方法，如果我找不到一个，我会找到两种方法的最好的代表（你发布的链接有一张2009年的文件，这是我想知道的），并检查他们的实验。

That's the best I got, so if I were you I would search for a paper that compares these two approaches, and if I couldn't find one, I would find the best representative of both approaches (the link you posted has a paper of 2009, that's old I guess), and check their experiments.

但要小心！为了比较最好的代表的方法，你需要确保每个纸张的实验是超级相关的，所使用的机器是相同的权力，所使用的数据是相同的性质和大小，等等。

But be careful! In order to compare the approaches by the best representatives, you need to make sure that the experiments of each paper are super-relevant, the machines used are of the same "powerness", the data used are of the same nature and size, and so on.

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