Salient Object Segmentation based on Linearly Combined Affinity Graphs

Caglar Aytekin, Alexandros Iosifidis, Serkan Kiranyaz, Moncef Gabbouj

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.

OriginalsprogEngelsk
Titel2016 23rd International Conference on Pattern Recognition, ICPR 2016
Antal sider6
ForlagIEEE
Publikationsdato1 jan. 2016
Sider3769-3774
Artikelnummer7900221
ISBN (Elektronisk)9781509048472
DOI
StatusUdgivet - 1 jan. 2016
Udgivet eksterntJa
Begivenhed23rd International Conference on Pattern Recognition - CANCUN INTERNATIONAL CONVENTION CENTER, Cancun, Mexico
Varighed: 4 dec. 20168 dec. 2016
http://www.icpr2016.org/site/

Konference

Konference23rd International Conference on Pattern Recognition
LokationCANCUN INTERNATIONAL CONVENTION CENTER
Land/OmrådeMexico
ByCancun
Periode04/12/201608/12/2016
Internetadresse

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