November 2019
This review is based on the pyimagesearch course.
Image descriptors and feature vectors quantify and abstractly represent an image using only a list of numbers.
- Feature vector: An abstraction of an image used to characterize and numerically quantify the contents of an image. Normally real, integer, or binary valued. Simply put, a feature vector is a list of numbers used to represent an image.
- this feature can be passed down to image classifier or image search engine
- Image descriptor: An image descriptor is an algorithm and methodology that governs how an input image is globally quantified and returns a feature vector abstractly representing the image contents.
- they tend to be much simpler than feature descriptors
- HoG, LBPs, Harlick texture
- Feature descriptor: A feature descriptor is an algorithm and methodology that governs how an input region of an image is locally quantified. A feature descriptor accepts a single input image and returns multiple feature vectors.
- SIFT, SURF, ORB, BRISK, BRIEF, and FREAK
Keypoint detection and feature extraction:
- keypoints are simply the (x, y)-coordinates of the interesting, salient regions of an image.
- feature extraction is the process of extracting multiple feature vectors, one for each keypoint How to use multiple features per image? Keypoint matching or bag-of-visual-words.
- Fast algorithm to detect corners
- Fusing Points and Lines for High Performance Tracking ICCV 2005
- A test is performed for a feature at a pixel p by examining a circle of 16 pixels (a Bresenham circle of radius 3) surrounding p. A feature is detected at p if the intensities of at least 12 contiguous pixels are all above or all below the intensity of p by some threshold, t.
- openCV example
- Quite fast (although slower than FAST), more accurate