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In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.
General In many problems the number of variables is very large. This can mean that processing of the data is slow, requires a lot of memory or that classification algorithm overfits to the training examples, thus generalizing poorly to new samples. Feature extraction is a general term for methods for constructing combinations of the variables which get around above problems but still describe the data sufficiently accurately. Best results are achieved when an expert constructs a set of application-dependent features. Nevertheless, if no such expert knowledge is available general dimensionality reduction techniques may help. These include: Image processing It can be used in the area of image processing which involves using algorithms to detect and isolate various desired portions or shapes (features) of a digitized image or video stream. It is particularly important in the area of Optical Character Recognition. Low-level Curvature Image motion Hough transform Flexible methods See also | ||||||||
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