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

Deep learning models can also be used for automatic feature extraction algorithms. Other common feature extraction techniques include:

  • Histogram of oriented gradients (HOG)
  • Speeded-up robust features (SURF)
  • Local binary patterns (LBP)
  • Haar wavelets
  • Color histograms

Once features have been extracted, they may be used to build machine learning models for accurate object recognition or object detection.

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

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.

See also: feature matching, object detection, image stabilization, image processing and computer vision, face recognition, image recognition, object detection, object recognition, digital image processing, optical flow, RANSAC, pattern recognition, point cloud, deep learning

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