使用监督或无监督学习的图像识别
我熟悉用于文本分类的监督学习方法（SVM、最大熵、贝叶斯分类器），但对于图像我不知道应该从哪里开始。
我有一组人类图像（仅限女性），我必须将它们分类为美丽或不美丽。我面临的第一个障碍是“特征选择”。我想把头发形状、肤色、眼睛形状作为特征，但它们变得太复杂而难以检测。 相比之下，OCR 似乎相对容易一些，因为形状可以设置为黑色和白色。白色格式并找到与已知符号的最佳匹配。
我也准备探索无监督学习方法，如果这更有用的话。 请给我一些指示，告诉我应该如何开始。 任何免费使用的库都会很棒（可以是任何语言）！
I am familiar with supervised Learning methods (SVM, Maximum Entropy, Bayes Classifiers) for textual classification, but for image I cannot figure out where I should start from.
I have a set of human images (exclusively women) whom I've to classify as being beautiful or not. The first hurdle I am facing is "Feature selection". I thought to take hair shape, complexion, eye shape as features but they are becoming too complex to detect.
OCR in comparison seems comparatively easier as the shapes can be put in black & white format and find best match with the known symbols.
I am also ready to explore unsupervised learning methods if that is more useful.
Please provide me pointers as to how should I begin with.
Any free to use libraries would be really great (could be in any language)!
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如果你想对美丽/不美丽进行分类，你需要使用监督学习，因为无监督学习不知道“美丽”的概念，你必须通过标签来引入它。
作为入门指南，ECCV 2010 上有一篇最近关于面部美容的论文（您可以在 http://dgray 上找到它.info/），在引言中引用了很多其他论文。
If you want to classify beatiful/not beautiful, you want to use supervised learning because unsupervised learning has no idea about the concept "beautiful", you have to introduce that via labels.
As a pointer to get you started, there is a recent paper on facial beauty on ECCV 2010 (you can find it on http://dgray.info/), in the introduction there are lots of references to other papers.