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In pattern recognition and in image processing, Feature extraction is a special form of dimensionality reduction. Pattern recognition is a field within the area of machine learning. ...
UPIICSA IPN - Binary image Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. ...
In statistics, dimensionality reduction is mapping a multidimensional space into a space of fewer dimensions. ...
When the input data to an algorithm is too large for being processing and it is suspected to be notoriously redundant (much data, but not much information) 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 features extraction. If the features extracted are carefully choosen it is expected that the features set 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.
General
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. Statistical classification is a type of supervised learning problem in which labeled training data is used to create a function that will correctly predict the label of future data. ...
Noisy (roughly linear) data is fit to both linear and polynomial functions. ...
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:
It has been suggested that this article or section be merged with Proper orthogonal decomposition. ...
Semidefinite embedding is a very recent (as of 2004) algorithm to perform non-linear dimensionality reduction. ...
// General Multifactor dimensionality reduction (MDR) is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable. ...
High dimensional data can be difficult to interpret. ...
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. ...
Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, invented in 1990 [1] by Scott Deerwester, Susan Dumais, George Furnas, Thomas Landauer, and Richard Harshman. ...
In statistics, the method of partial least squares bears some relation to principal component analysis; instead of finding the hyperplanes of maximum variance, it finds a linear model describing some predicted variables in terms of other observable variables. ...
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. UPIICSA IPN - Binary image Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image. ...
In mathematics, computing, linguistics, and related disciplines, an algorithm is a finite list of well-defined instructions for accomplishing some task that, given an initial state, will terminate in a defined end-state. ...
A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels. ...
Streaming media is multimedia that is continuously received by, and normally displayed to, the end-user while it is being delivered by the provider. ...
Low-level The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. ...
Corner detection or the more general terminology interest point detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. ...
In the area of computer vision, blob detection refers to visual modules that are aimed at detecting points and/or regions in the image that are either brighter or darker than the surrounding. ...
In a 2-D function, a (bright) ridge is a connected set of points that are maximal in at least one dimension. ...
Scale-invariant feature transform (or SIFT) is a computer vision algorithm for extracting distinctive features from images, to be used in algorithms for tasks like matching different views of an object or scene (e. ...
Curvature A plot showing 100 random numbers with a hidden sine function, and an autocorrelation of the series on the bottom. ...
Image motion It has been suggested that Motion detector be merged into this article or section. ...
Optical flow is a concept which is close to, but not identical with the motion of objects within a visual representation. ...
Shape Based
Thresholding Blob extraction is an image segmentation technique that categorizes the pixels in an image as belonging to one of many discrete regions. ...
Template matching
- Lines
- Circles/Ellipse
- Arbitrary shapes (Generalized Hough Transform)
The Hough transform (pronounced ) is a feature extraction technique used in digital image processing. ...
Flexible methods - Deformable, parameterized shapes
- Active contours (snakes)
References - JMLR Special Issue on Variable and Feature Selection
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