机器学习之HOG算法
Histogram of Oriented Gradients (HOG) Descriptor
Histogram of oriented gradients (HOG) is a feature descriptor used to detect objects in computer vision and image processing. The HOG descriptor technique counts occurrences of gradient orientation in localized portions of an image - detection window, or region of interest (ROI).
Implementation of the HOG descriptor algorithm is as follows:
- Divide the image into small connected regions called cells, and for each cell compute a histogram of gradient directions or edge orientations for the pixels within the cell.
- Discretize each cell into angular bins according to the gradient orientation.
- Each cell's pixel contributes weighted gradient to its corresponding angular bin.
- Groups of adjacent cells are considered as spatial regions called blocks. The grouping of cells into a block is the basis for grouping and normalization of histograms.
- Normalized group of histograms represents the block histogram. The set of these block histograms represents the descriptor.
The following figure demonstrates the algorithm implementation scheme:
![](https://software.intel.com/sites/default/files/did_feeds_images/0EF01A88-F874-4ECB-B2B6-3ADC38636CD4/0EF01A88-F874-4ECB-B2B6-3ADC38636CD4-imageId=6A72422F-6619-4E3D-92A2-BC640A6572CC.png)
Computation of the HOG descriptor requires the following basic configuration parameters:
- Masks to compute derivatives and gradients
- Geometry of splitting an image into cells and grouping cells into a block
- Block overlapping
- Normalization parameters