Feature extraction
Image processing
One very important area of application is image processing, in which algorithms are used 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.
Implementations
Many data analysis software packages provide for feature extraction and dimension reduction. Common numerical programming environments such as MATLAB, SciLab, NumPy, scikit-learn and the R language provide some of the simpler feature extraction techniques (e.g. principal component analysis) via built-in commands. More specific algorithms are often available as publicly available scripts or third-party add-ons. There are also software packages targeting specific software machine learning applications that specialize in feature extraction.[1]
See also
- Cluster analysis
- Dimensionality reduction
- Feature detection
- Feature selection
- Data mining
- Connected-component labeling
- Segmentation (image processing)
- Space mapping
- Dynamic texture
- Radiomics
References
- ^ See, for example, https://reality.ai/
 | This article needs additional citations for verification. (January 2016) |
Rustum, Rabee, Adebayo Adeloye, and Aurore Simala. "Kohonen self-organising map (KSOM) extracted features for enhancing MLP-ANN prediction models of BOD5." In International Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management-24th General Assembly of the International Union of Geodesy and Geophysics (IUGG), pp. 181-187. 2007.