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Visual Saliency by Extended Quantum Cuts

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE SIGNAL PROCESSING SOCIETY
Pages1692-1696
Number of pages5
ISBN (Print)978-1-4799-8339-1
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Abstract

In this study, we propose an unsupervised, state-of-the-art saliency map generation algorithm which is based on a recently proposed link between quantum mechanics and spectral graph clustering, Quantum Cuts. The proposed algorithm forms a graph among superpixels extracted from
an image and optimizes a criterion related to the image boundary, local contrast and area information. Furthermore, the effects of the graph connectivity, superpixel shape irregularity, superpixel size and how to determine the affinity between superpixels are analyzed in detail. Furthermore, we introduce a novel approach to propose several saliency maps. Resulting saliency maps consistently achieves a state-of-the-art performance in a large number of publicly available benchmark datasets in this domain, containing
around 18k images in total.

Publication forum classification

Field of science, Statistics Finland