NeRD: a Neural Response Divergence Approach to Visual Salience Detection

In this paper, a novel approach to visual salience detection via Neural Response Divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low level cues that can be used to compute image region distinc...

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Bibliographic Details
Published in:arXiv.org 2016-02
Main Authors: Shafiee, M J, Siva, P, Scharfenberger, C, Fieguth, P, Wong, A
Format: Article
Language:English
Subjects:
Complexity
Computer vision
Divergence
Image detection
Low level
Neural networks
Object recognition
Salience
Visual perception
Online Access:Get full text
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Summary:In this paper, a novel approach to visual salience detection via Neural Response Divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low level cues that can be used to compute image region distinctiveness. Based on this concept , an efficient visual salience detection framework is proposed using deep convolutional StochasticNets. Experimental results using CSSD and MSRA10k natural image datasets show that the proposed NeRD approach can achieve improved performance when compared to state-of-the-art image saliency approaches, while the attaining low computational complexity necessary for near-real-time computer vision applications.
ISSN:2331-8422