Biologically-inspired characterization of sparseness in natural images
Abstract
Natural images follow statistics inherited by the structure of our physical (visual) environment. In particular, a prominent facet of this structure is that images can be described by a relatively sparse number of features. We designed a sparse coding algorithm biologically-inspired by the architecture of the primary visual cortex. We show here that coefficients of this representation exhibit a heavy-tailed distribution. For each image, the parameters of this distribution characterize sparseness and vary from image to image. To investigate the role of this sparseness, we designed a new class of random textured stimuli with a controlled sparseness value inspired by our measurements on natural images. Then, we provide with a method to synthesize random textures images with a given statistics for sparseness that matches that of some given class of natural images and provide perspectives for their use in neurophysiology.
Keywords
Cite
@article{arxiv.1702.02485,
title = {Biologically-inspired characterization of sparseness in natural images},
author = {Laurent U Perrinet},
journal= {arXiv preprint arXiv:1702.02485},
year = {2017}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1611.06834