Saliency map using features derived from spiking neural networks of primate visual cortex

TL;DR

This paper introduces a biologically inspired saliency map generation method using features from spiking neural networks modeled after primate visual cortex, applied to benchmark images.

Contribution

It presents a novel framework combining SNNs and receptive field models for saliency detection, inspired by biological vision systems.

Findings

Effective saliency maps generated from SNN features

Applicable to standard benchmark images

Demonstrates biological plausibility in computational vision

Abstract

We propose a framework inspired by biological vision systems to produce saliency maps of digital images. Well-known computational models for receptive fields of areas in the visual cortex that are specialized for color and orientation perception are used. To model the connectivity between these areas we use the CARLsim library which is a spiking neural network(SNN) simulator. The spikes generated by CARLsim, then serve as extracted features and input to our saliency detection algorithm. This new method of saliency detection is described and applied to benchmark images.