  Fast and invariant feature extraction is crucial in certain computer vision
applications where the computation time is constrained in both training and
testing phases of the classifier. In this paper, we propose a nature-inspired
dimensionality reduction technique for fast and invariant visual feature
extraction. The human brain can exchange the spatial and spectral resolution to
reconstruct missing colors in visual perception. The phenomenon is widely used
in the printing industry to reduce the number of colors used to print, through
a technique, called color dithering. In this work, we adopt a fast
error-diffusion color dithering algorithm to reduce the spectral resolution and
extract salient features by employing novel Hessian matrix analysis technique,
which is then described by a spatial-chromatic histogram. The computation time,
descriptor dimensionality and classification performance of the proposed
feature are assessed under drastic variances in orientation, viewing angle and
illumination of objects comparing with several different state-of-the-art
handcrafted and deep-learned features. Extensive experiments on two publicly
available object datasets, coil-100 and ALOI carried on both a desktop PC and a
Raspberry Pi device show multiple advantages of using the proposed approach,
such as the lower computation time, high robustness, and comparable
classification accuracy under weakly supervised environment. Further, it showed
the capability of operating solely inside a conventional SoC device utilizing a
small fraction of the available hardware resources.
