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Feature extraction
From Wikipedia, the free encyclopedia
In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction.
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Feature extraction involves simplifying the amount of resources required to describe a large set of data accurately. When performing analysis of complex data one of the major problems stems from the number of variables involved. Analysis with a large number of variables generally requires a large amount of memory and computation power or a classification algorithm which overfits the training sample and generalizes poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy.
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:
- Principal components analysis
- Semidefinite embedding
- Multifactor dimensionality reduction
- Nonlinear dimensionality reduction
- Isomap
- Kernel PCA
- Latent semantic analysis
- Partial least squares
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.
- Edge direction, changing intensity, autocorrelation.
- Motion detection. Area based, differential approach. Optical flow.
- Lines
- Circles/Ellipse
- Arbitrary shapes (Generalized Hough Transform)
- Deformable, parameterized shapes
- Active contours (snakes)
