  Hyperdimensional Computing (HDC) is a brain-inspired and light-weight machine
learning method. It has received significant attention in the literature as a
candidate to be applied in the wearable internet of things, near-sensor
artificial intelligence applications and on-device processing. HDC is
computationally less complex than traditional deep learning algorithms and
typically achieves moderate to good classification performance. A key aspect
that determines the performance of HDC is the encoding of the input data to the
hyperdimensional (HD) space. This article proposes a novel light-weight
approach relying only on native HD arithmetic vector operations to encode
binarized images that preserves similarity of patterns at nearby locations by
using point of interest selection and local linear mapping. The method reaches
an accuracy of 97.35% on the test set for the MNIST data set and 84.12% for the
Fashion-MNIST data set. These results outperform other studies using baseline
HDC with different encoding approaches and are on par with more complex hybrid
HDC models. The proposed encoding approach also demonstrates a higher
robustness to noise and blur compared to the baseline encoding.
