Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

Background prior-based salient object detection via deep reconstruction residual

Han, Junwei and Zhang, Dingwen and Hu, Xintao and Guo, Lei and Ren, Jinchang and Wu, Feng (2015) Background prior-based salient object detection via deep reconstruction residual. IEEE Transactions on Circuits and Systems for Video Technology, 25 (8). pp. 1309-1321. ISSN 1051-8215

[img]
Preview
Text (Han-etal-IEEE-TOCASFVT-2015-Background-prior-based-salient-object-detection-via-deep)
Han_etal_IEEE_TOCASFVT_2015_Background_prior_based_salient_object_detection_via_deep.pdf - Accepted Author Manuscript

Download (10MB) | Preview

Abstract

Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand designed features and their distinctiveness measured using local or global contrast. Although these approaches have shown effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learnt in an unsupervised and bottom up manner. Afterwards, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations on three benchmark datasets and comparisons with 9 state-of-the-art algorithms demonstrate the superiority of the proposed work.