In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.

General

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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 archived 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:

• Principal components analysis
• Semidefinite embedding
• Multifactor dimensionality reduction
• Nonlinear dimensionality reduction
• Isomap
• Kernel PCA
• Latent semantic analysis
• Partial least squares

Image processing

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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

• Edge detection
• Corner detection
• Scale-invariant feature transform

Curvature

• Edge direction, changing intensity, autocorrelation.

Image motion

• Motion detection. Area based, differential approach. Optical flow.

Shape Based

Thresholding

Template matching

Hough transform

• Lines
• Circles/Ellipse
• Arbitrary shapes (Generalized Hough Transform)

Flexible methods

• Deformable, parameterized shapes
• Active contours (snakes)

References

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• JMLR Special Issue on Variable and Feature Selection

See also

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• Dimensionality reduction
• Feature selection
• Data mining

Computer vision stubs

Machine learning

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