Feature Extraction

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

When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (e.g. the same measurement in both feet and meters) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input.

Read more about Feature ExtractionGeneral, Image Processing, Feature Extraction in Software

Other articles related to "feature extraction, extraction, features, feature":

Feature Extraction in Software
... analysis software packages provide for feature extraction and dimension reduction ... environments such as MATLAB, SciLab, NumPy and the R language provide some of the simpler feature extraction techniques (e.g ...
Release History - WinZip
... WinZip 3.2 added Built-in ZIP Extraction so that PKUNZIP is not needed for most ZIP extract operations ... WinZip 4.0 added support for PKZIP 2.0, including built-in ZIP extraction, support for the ARJ file format, new toolbar with descriptive pictorial buttons, added a 3-D look to all dialog boxes ... Note that two advanced features, disk-spanning and encryption, still require PKZIP ...
Handwriting Recognition - Off-line Recognition - Character Recognition - Feature Extraction
... Feature extraction works in a similar fashion to neural network recognizers however, programmers must manually determine the properties they feel are important ...
List Of Computer Vision Topics - Feature Extraction
... linking Harris corner detector Random sample consensus (RANSAC) Scale-invariant feature transform (SIFT) ...

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