Vectorized PyTorch implementation of M. Cheon et al., Rotation Invariant Histogram of Oriented Gradients, International Journal of Fuzzy Logic and Intelligent Systems, vol. 11, no. 4, December 2011.
pip install rihog
import rihog
import torch
img_b: torch.Tensor # shape: (b 1 h w)
# class-based interface
rihog_fextr = rihog.RIHOG(...)
img_rihog_b = rihog_fextr(img_b) # shape: (b wc nc*bc)
# wc: window count, nc: neighborhood count, bc: bin count
# functional interface
img_rihog_b = rihog.rihog(img_b, ...) # shape: (b wc nc*bc)
Comparison of RIHOG1 and RIHOG3 against HOG and SIFT on the Original Brodatz Texture database. All algorithms are evaluated on the cosine distance-weighted k-NN accuracy computed from the average feature of each image, across three sizes of the feature vector (dimension). Following the evaluation procedure from the paper, 12 rotated versions are generated for each image (30° steps from 0° to 330°) and split into training set (first N rotations) and testing set (remaining rotations).
All benchmarks are run on the following hardware:
- CPU: i9-11900KF @ 3.50GHz
- RAM: 64GB @ 2400Mhz CL 17
- GPU: RTX 3090 24GiB
Following is a run time comparison of the vectorized implementation on CPU and CUDA against the naive (non-vectorized) implementation across three hyper-parameter spaces: number of neighborhoods computed for each pixel (nbhd_steps parameter), batch size and image size.
