Spatiotemporal Saliency Estimation by Spectral Foreground Detection

Çaglar Aytekin, Horst Possegger, Thomas Mauthner, Serkan Kiranyaz, Horst Bischof, Moncef Gabbouj

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.
Original languageEnglish
Pages (from-to)82-95
JournalIEEE Transactions on Multimedia
Volume20
Issue number1
DOIs
Publication statusPublished - 2018

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