Image-based feature engineering
Instruction and application
In ProgressImage-based feature engineering
Images are dense, high-dimensional inputs filled with colour, texture, edges and shapes. Before a model can use them well, those visual signals need to be expressed in a structured way.
Colour histograms and colour spaces
Colour histograms count pixels across intensity ranges, giving the model a summary of colour distribution. In many workflows, colour is represented in spaces like HSV or LAB instead of raw RGB so hue, brightness and saturation are easier to reason about.
This can be useful in tasks such as distinguishing red fruit from green fruit or spotting colour shifts that indicate defects.
Texture descriptors
Texture features help models distinguish surfaces and visual patterns.
- GLCM captures relationships between neighbouring pixel intensities.
- LBP highlights local roughness and repeated surface patterns.
These are especially useful in medical imaging, materials analysis and quality control.
Edge detection and contour analysis
Edge detectors such as Canny or Sobel highlight boundaries, while contour analysis traces the outlines of objects. These methods help with segmentation, shape analysis and object isolation.
Feature descriptors
Descriptors such as SIFT, SURF and ORB identify distinctive keypoints and describe the local image pattern around them. They are valuable when matching the same object across different scales, rotations or lighting conditions.
Histogram of Oriented Gradients (HOG)
HOG captures the distribution of edge directions across small parts of an image. It is a strong classic technique for describing object shape and is widely used in tasks like pedestrian detection.
Why classic image features still matter
Even in a deep learning world, traditional image features remain useful when you need speed, interpretability, smaller datasets or simpler production pipelines.