A cortex-like model for rapid object recognition using feature-selective hashing

Building models by mimicking the structures and functions of visual cortex has always been a major approach to implement a human-like intelligent visual system. Several feed-forward hierarchical models have been proposed and perform well on invariant feature extraction. However, less attention has been given to the biologically plausible feature matching model which mimics higher levels of the ventral stream. In this work, with the inspirations from both neuroscience and computer science, we propose a framework for rapid object recognition and present the feature-selective hashing scheme to model the memory association in inferior temporal cortex. The experimental results on 1000-class ALOI dataset demonstrate its efficiency and scalability of learning on feature matching. We also discuss the biological plausibility of our framework and present a bio-plausible network mapping of the feature-selective hashing scheme.