A Cortex-like Model For Animal Recognition Based On Texture Using Feature-Selective Hashing

Building a model that can mimic the brainʹs cortex has always been a major goal, because the human brain recognizes objects in terms of speed, reliability and flexibility that are always unique pattern for machine vision systems. In this paper, we are inspired by neuroscience and computer science that have designed a framework that can be fast and accurate emulation of the inferior temporal cortex with feature selective hashing to recognize animals. We worked on KTH database containing 1239 images in 13 classes that took photos from animals in wild.