为什么要在感知哈希中创建哈希？
我一直在研究 http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html&comment-subscribed#feedback 和我在处理图像后尝试从图像的位创建哈希时遇到困难。如果您对根据图像像素创建的二进制字符串进行哈希处理，然后查看汉明距离来分析照片的差异程度，那么与在原始二进制字符串上执行汉明距离相比，创建哈希值执行汉明距离有什么好处呢？创建哈希仅仅是为了加快速度吗？
我对哈希了解不多。我认为在这种情况下它们充当几乎相同照片的过滤机制？但这种过滤不是通过缩小照片并将其转换为灰度来完成的吗？
I've been working through the examples at http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html&comment-submitted#feedback and I got stuck trying to create a hash from the bits of the image after it's processed. If you hash the binary string created from the pixels of an image and then look at the hamming distance to analyze how different the photos are, what good is creating a hash doing a hamming distance vs. doing a hamming distance on the raw binary string? Is the hash created merely to speed things up?
I don't know much about hashes. I assume in this case they act as a filtering mechanism for nearly identical photos? But isn't this filtering accomplished by downsizing the photo and converting it to greyscale?
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（1）
博文中提出的想法是如何识别相似的图片。目标是丢失正确的信息，以便剩下的信息有意义且易于比较。所以有两个方面：比较的速度和准确度。如果将图片缩小为 8x8 黑白（即 64 位信息），那么无论您将其称为“原始咬串”还是“长哈希”（好吧，正如@Blender 指出的），都没有关系在该术语的常规使用中，它实际上并不是一个哈希值）。重要的是如何减少它以及留下什么信息和丢失什么信息。
Idea presented in the blog post is how to recognize similar pictures. And goal is to lose right kind of information so that what is left is significant and easy to compare. So there are two aspects: how fast and how accurate can you compare. If you reduce your picture to 8x8 black and white (that is 64 bits of information), then it doesn't matter if you've call it a "raw bite string" or a "long hash" (well, as @Blender noted it's not really a hash in conventional use of the term). Important thing is how to reduce it and what information is left and what is lost.