Google的图片颜色搜索如何工作？ [英] How does Google's image color search work?
问题描述
假设我查询
我得到所有的黑色天空，算法实际上是如何工作的？
and I get all the black color sky, how actually does the algorithm behind work?
推荐答案
基于由Google工程师Henry Rowley，Shumeet Baluja和Yushi Jing发表的这篇论文，似乎你的问题中关于识别图像颜色的最重要的含义与谷歌的saferank算法的图片相关，可以检测肉体 -
Based on this paper published by Google engineers Henry Rowley, Shumeet Baluja, and Dr. Yushi Jing, it seems the most important implication of your question about recognizing colors in images relates to google's "saferank" algorithm for pictures that can detect flesh-tones without any text around it.
本文首先描述经典方法，通常基于标准化颜色亮度，然后使用 Gaussian Distribution ，或使用使用像素中的RGB值建立的三维直方图（每种颜色是一个8位整数值，从0-255表示多少。的颜色被包括在像素中）。还引入了依赖于诸如亮度（通常不正确地称为亮度）等属性的方法，这是从给定图像到裸眼的发光强度的密度。
The paper begins by describing by describing the "classical" methods, which are typically based on normalizing color brightness and then using a "Gaussian Distribution," or using a three-dimensional histogram built up using the RGB values in pixels (each color is a 8bit integer value from 0-255 representing how much . of that color is included in the pixel). Methods have also been introduced that rely on properties such as "luminance" (often incorrectly called "luminosity"), which is the density of luminous intensity to the naked eye from a given image.
Google文章提到，他们需要使用算法处理大约10 ^ 9张图片，因此需要尽可能高效。为了实现这一点，它们对ROI（感兴趣区域）执行大多数计算，ROI是在图像中居中的矩形，并且在所有侧上插入1/6的图像尺寸。一旦他们确定了投资回报率，他们有许多不同的算法，然后应用于图像包括面部检测algs，颜色恒定algs，和其他，作为一个整体找到的图像的统计趋势着色，最重要的是在统计分布中找到具有最高频率的色调。
The google paper mentions that they will need to process roughly 10^9 images with their algorithm so it needs to be as efficient as possible. To achieve this, they perform the majority of their calculations on an ROI (region of interest) which is a rectangle centered in the image and inset by 1/6 of the image dimensions on all sides. Once they've determined the ROI, they have many different algorithms that are then applied to the image including Face-Detection algs, Color Constancy algs, and others, which as a whole find statistical trends in the image's coloring and most importantly find the color shades with the highest frequency in the statistical distribution.
它们使用其他特征，例如熵，边缘检测和纹理定义到
为了从图像中提取线，他们使用在肤色边缘计算的概率Hough变换（Brasski，2000）的OpenCV实现（Bradski，2000）（Kiryati et al。，1991）其允许他们找到可能不是身体部分的直线，并且还允许他们更好地确定哪些颜色在图像中是最重要的，这是它们的图像颜色搜索中的关键因素。
They use other features such as Entropy , Edge-Detection, and texture-definitions to In order to extract lines from the images, they use the OpenCV implementation (Bradski, 2000) of the probabilistic Hough transform (Kiryati et al., 1991) computed on the edges of the skin color connected components, which allows them to find straight lines which are probably not body parts and additionally allows them to better determine which colors are most important in an image, which is a key factor in their Image Color Search.
有关本主题的技术性（包括数学方程式等）的更多信息，请阅读开头链接的Google论文，并查看其网站的Research部分。
For more on the technicalities of this topic including the math equations and etc, read the google paper linked to in the beginning and look at the Research section of their web site.
非常有趣的问题和主题！
Very interesting question and subject!
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