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Fast and invariant feature extraction is crucial in certain computer visionapplications where the computation time is constrained in both training andtesting phases of the classifier. In this paper, we propose a nature-inspireddimensionality reduction technique for fast and invariant visual featureextraction. The human brain can exchange the spatial and spectral resolution toreconstruct missing colors in visual perception. The phenomenon is widely usedin the printing industry to reduce the number of colors used to print, througha technique, called color dithering. In this work, we adopt a fasterror-diffusion color dithering algorithm to reduce the spectral resolution andextract 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 proposedfeature are assessed under drastic variances in orientation, viewing angle andillumination of objects comparing with several different state-of-the-arthandcrafted and deep-learned features. Extensive experiments on two publiclyavailable object datasets, coil-100 and ALOI carried on both a desktop PC and aRaspberry Pi device show multiple advantages of using the proposed approach,such as the lower computation time, high robustness, and comparableclassification accuracy under weakly supervised environment. Further, it showedthe capability of operating solely inside a conventional SoC device utilizing asmall fraction of the available hardware resources.

Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction