启动语义图像识别
如何识别合适的图像？
为了促进、启用和简化针对 gae 的照片和图像审核和管理，我尝试开始使用基本的 Python 图像识别，即图像的基本语义信息，以阻止可疑的材料，直到人类可以判断它，并批准最多的内容都很好。测试批次> 10 000 张图像只有一张或只有极少数，因此避免误报自然是好的。我找到了以下链接，提前感谢大家的建议、意见和推荐。基本上，审核将显示许多图像，只有一个“确定”按钮，反之亦然，默认“确定”和一个“不批准”按钮，具体取决于默认决定（默认可能会发布所有内容，如果有些不合适，则临时（人类）不批准，因为绝对主要部分> 99 % 材料合适） 链接文本
How to recognize (in)appropriate images?
To facilitate, enable and easify photo and image moderation and administration targeting gae, I try get started with basic python image recognition ie basic semantic information what the image looks like to hold back doubtful material until human can judge it, and to approve the most that are good. A test batch > 10 000 images had one or just a very few so avoiding false positives naturally is good. I found the following links to follow and thank you all in advance for all advice, suggestions and recommendations. Very basically moderation will display a number of images and just a button "ok" or viceversa default "ok" and a button "Disapprove" depending on default decision (default probably publish everything and ad hoc (human) disapproval if some unsuitable since the absolute major part > 99 % material is suitably good)
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我相信您会想从这里开始
http://en.wikipedia.org/wiki/ feature_detection_%28computer_vision%29
，然后温习统计理论，阅读有关该主题的任何论文。
I believe you will want to start here
http://en.wikipedia.org/wiki/Feature_detection_%28computer_vision%29
and then brush up on your statistical theory, reading any papers on the topic.
在Python中你总是可以：
因为当涉及到色情内容时，他们一看到就知道了。
抛开平庸的笑话不谈，我会开发一堆模糊图像识别器来匹配简单的东西（比如图像有多少是由肤色组成的？）。此时您可能会想出大量可疑变量 - 这是困难的部分。然后使用分类和回归树来实现实际的决策引擎。使用训练样本对其进行训练，然后进行交叉样本验证以了解误报/漏报。
In python you could always:
Because when it comes to pornography, they know it when they see it.
Mediocre jokes aside, I would develop a bunch of fuzzy image recognizers that match easy things (like how much of the image is made up of a skin color tone?). You could probably come up with a good amount of suspicious variables at this point - this is the hard(ish) part. Then use Classification and Regression Trees to implement the actual decision engine. Train it with your training sample, then do cross-sample validation to get a sense of the false positives/negatives.