- low-level feature:通常是指图像中的一些小的细节信息，例如边缘(edge)，角(corner), 颜色(color),像素(pixels),梯度(gradients)等，这些信息可以通过滤波器、SIFT或HOG获取；
- hight-level feature:是建立在low level feature之上的，可以用于图像中目标或物体形状的识别和检测，具有更丰富的语义信息。
通常卷积神经网络中都会使用这两种类型的features:卷积神经网络的前几层学习Low level feature, 后几层学习的是high level feature.
Quora上面也有这么一段解释：
Low-level features are minor details of the image, like lines or dots, that can be pickup by , say, a convolutional filter (for reaaly low-level things) or SIFT or HOG (for more abstract things like edges).
High levle features are built on top of low-level features to detect objects and shapes in the image.