Feature detectors versus descriptors
In image processing, (local) features refer to a group of key/salient points or information relevant to an image processing task, and they create an abstract, more general (and often robust) representation of an image. A family of algorithms that choose a set of interest points from an image based on some criterion (for example, cornerness, local maximum/minimum, and so on, that detect/extract the features from an image) are called feature detectors/extractors.
On the contrary, a descriptor consists of a collection of values to represent the image with the features/interest points (for example, HOG features). Feature extraction can also be thought of as an operation that transforms an image into a set of feature descriptors, and, hence, a special form of dimensionality reduction. A local feature is usually formed by an interest point and its descriptor together.
Global features from the whole image (for example, image histogram) are often not desirable...
