A cortex-like model for rapid object recognition using feature-selective hashing
Journal
Proceedings of the International Joint Conference on Neural Networks
Pages
996-1002
Date Issued
2011
Author(s)
Abstract
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. © 2011 IEEE.
Event(s)
2011 International Joint Conference on Neural Network, IJCNN 2011
Other Subjects
Building model; Data sets; Feature matching; Feed-Forward; Hierarchical model; Inferior temporal cortices; Invariant feature extraction; Its efficiencies; Network mapping; Ventral streams; Visual cortexes; Visual systems; Computer vision; Hierarchical systems; Neural networks; Object recognition; Feature extraction
Type
conference paper