如何将图像直方图存储到数据库中并能够执行搜索
我需要编写网络应用程序，用户将能够根据图像颜色执行搜索。我的问题是如何存储颜色数据？我认为最好的解决方案是减少图像颜色并为每个 r、g 和 b 通道准备直方图，但我不知道如何设计数据库。我想使用 MySQL 数据库管理系统。 有人能指出我正确的方向吗？
问候
I need to write webapp where user will be able to perform search based on images colors. My question is how to store color data? I think that best solution will be to reduce image colors and prepare histogram for each r, g and b channel but I don't have idea how to design database. I want to use MySQL DBMS.
Could someone point me into right direction?
Regards
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关于存储直方图数据，我想到了一些想法。显而易见的选择是使用一个表（或三个用于单独的 R/G/B 通道）来表示（标准化）直方图，每个 bin 有一列。如果您使用 24 位颜色（8 位/通道），则可以将每个通道分为 16 个容器（[0-15]、...、[240-255]），并在每列中存储像素百分比掉进那个垃圾箱里了。
像这样：
通过这种设计，每个图像的整个（标准化）直方图将表示为表中的一行。
查询有点具有挑战性——您必须动态生成它们，以便为感兴趣的值范围插入正确的列名称。
也许更好的方法是使用 HistogramBins 表，其中每个图像和每个 bin 都有一个行条目：
使用这种存储格式，可以准备查询而不是动态计算。我不清楚是否应该像我一样对组件进行分解，或者是否应该为所有三个颜色组件的“bin 1”存储一行。我可能想写一些查询，看看什么最适合您的应用程序。
另外，我一直说“标准化”的原因是这个方案将使你的分箱独立于图像大小。
希望这有助于您入门。让我们知道您最终会得到什么！
A couple of ideas come to mind for storing histogram data. The obvious choice is to have one table (or three for separate R/G/B channels) that represents the (normalized) histogram, with a column for each bin. If you're in 24 bit color (8 bits/channel), you could break each channel into 16 bins ([0-15], ..., [240-255]), and in each column store the percentage of pixels that fell into that bin.
Something like this:
With this design, the entire (normalized) histogram for each image would be represented as a single row in the table.
Queries would be a bit challenging--you'd have to generate them dynamically to plug in the right column names for the value range of interest.
Perhaps a better way would be a HistogramBins table with a row entry for each image and each bin:
With that storage format, queries could be prepared rather than dynamically computed. It's not clear to me whether the components should be broken out as I did or if you should store one row for "bin 1" of all three color components. I'd probably want to write some queries and see what felt best for your application.
Also, the reason I keep saying 'normalized' is that this scheme would make your binning independent of image size.
Hope this helps get you started. Let us know what you end up with!
RGB 值对人类感知没有任何意义，但可以轻松转换为色相、饱和度、亮度这对人们来说更加明智。不幸的是，饱和度和亮度非常直观：更丰富：更苍白和更亮：更暗，但我们没有自然的颜色顺序，因此色调表示为围绕圆的任意度数。在实践中，要求人们进行精细的色调辨别是相当困难的，尤其是在寻找尚未见过的东西时。因此，您可能希望将类别限制为 中的六边形顶点图“a”。
那么你就会遇到一个问题：照片的代表色是什么？一半是蓝天、一半是棕褐色的沙子的图像是蓝色还是棕褐色？您选择主色调吗？您可能想要应用巨大的高斯模糊，然后对所得色调进行平均。您可能需要进一步完善您的问题和目标。
对感知模型的闲思
即使 HSL 也有其描述性的局限性。我上面提到的“棕褐色”是沙子的颜色。大多数读者可能在感知或命名它时完全没有问题，但除非您有太多使用颜色的经验，否则棕褐色的色调是橙色但苍白（不太饱和）和明亮（较高值）是相当不明显的。大约三分之一的色相环专用于绿色等。
RGB values have no meaning to human perception but they can be easily converted to Hue, Saturation, Luminance which is more sensible to people. Unfortunately, saturation and luminance are pretty intuitive: richer:paler and lighter:darker, but we have no natural ordering for colors so hue is expressed as an arbitrary number of degrees around a circle. In practice, asking people to make fine hue discriminations, especially when searching for something yet unseen is pretty hard. Therefore, you might want to limit your categories to the vertices of the hexagon in figure "a".
Then you run into the question of what is the representative color of a photograph? Is the image that is half blue sky and half tan sand blue or tan? Are you picking a dominant hue? You might want to apply a huge Gaussian blur and then average the resultant hues. You probably need to refine your question and goals further.
Idle musing on perceptual models
Even HSL has its descriptive limitations. I mention "tan" above as the color of sand. Most readers probably have no problem at all perceiving or naming it, but unless you have too much experience playing with color, it is pretty non-obvious that the hue of tan is orange but pale (less saturated) and bright (higher value). And about a third of the hue circle is devoted to greens, etc.