Contents
What do you need to know about SIFT features?
We want features that correspond to “parts” of images, at a more holistic level than raw pixels. We want features that are not sensitive to changes in image resolution, scale, rotation, changes in illumination (eg, position of lights). The SIFT algorithm will do this.
How to create a bag of features descriptor?
Obtain the set of bags of features. Select a large set of images. Extract the SIFT feature points of all the images in the set and obtain the SIFT descriptor for each feature point that is extracted from each image.
How to extract SIFT descriptors from large number of images?
You extract SIFT descriptors from a large number of images, similar to those you wish classify using bag-of-features. (Ideally this should be a separate set of images, but in practice people often just get features from their training image set.)
How is a SIFT feature described in OpenCV?
SIFT is one of most popular feature extraction and description algorithms. It extracts blob like feature points and describe them with a scale, illumination, and rotational invariant descriptor. The above image shows how a SIFT point is described using a histogram of gradient magnitude and direction around the feature point.
How is a gradient descriptor used in SIFT?
However, SIFT takes it a step further by instead generating descriptors for each keypoint not with raw intensity values, but by computing the gradient of each pixel (a gradient is a vector of partial derivatives, so for a pixel its gradient describes how intensity changes as X and Y change).
Is the SIFT function included in OpenCV?
SIFT is a patented algorithm and isn’t included in many distributions of OpenCV. Also, OpenCV’s function names change drastically between versions, and old code breaks! It will save you a lot of pain if you’re on the same version as me (v3.1.0) for this tutorial.
How is histogram of oriented gradients used in vbow?
Then it bins the gradients into 8 directions (like N, NE, E, SE, S, SW, W, NW) and builds an 8-bin histogram. One advantage of this process, called histogram of oriented gradients, is that it is very robust against changes in illumination conditions across images.