Feature Extraction

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Feature extraction for compact representation of image data in computer vision

Feature extraction a type of dimensionality reduction that efficiently represents interesting parts of an image as a compact feature vector. This approach is useful when image sizes are large and a reduced feature representation is required to quickly complete tasks such as image matching and retrieval.

Feature detection, feature extraction, and matching are often combined to solve common computer vision problems such as object detection and recognition, content-based image retrieval, face detection and recognition, and texture classification.

Detecting an object in a cluttered scene using a combination feature detection,feature extraction and matching

Detecting an object (left) in a cluttered scene (right) using a combination feature detection,feature extraction and matching. See example for details.

Common feature extraction techniques include Histogram of Oriented Gradients (HOG), Speeded Up Robust Features (SURF), Local Binary Patterns (LBP), Haar wavelets, and color histograms.

For details see Computer Vision System Toolbox and Image Processing Toolbox. Both toolboxes are for use with MATLAB.

Histogram of Oriented Gradients feature extraction of image

Histogram of Oriented Gradients (HOG) feature extraction of image (top). Feature vectors of different sizes are created to represent the image by varying cell size (bottom). See example for details.

Examples and How To

Software Reference

See also: feature detection, feature matching, object detection, image stabilization, image and video processing, face recognition, image recognition, object detection, object recognition, digital image processing, Optical Flow, ransac, pattern recognition