Spatiotemporal Saliency Estimation by Spectral Foreground Detection

Ç. Aytekin, H. Possegger, T. Mauthner, S. Kiranyaz, H. Bischof, M. Gabbouj

    Research output: Contribution to journalArticleScientificpeer-review

    13 Citations (Scopus)


    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
    Number of pages14
    JournalIEEE Transactions on Multimedia
    Issue number1
    Early online date8 Jun 2017
    Publication statusPublished - 2018
    Publication typeA1 Journal article-refereed


    • Computational modeling
    • Electronic mail
    • Estimation
    • Image color analysis
    • Object detection
    • Optimization
    • Spatiotemporal phenomena
    • Salient object detection
    • foreground detection
    • saliency
    • spatiotemporal
    • spectral graph theory

    Publication forum classification

    • Publication forum level 2


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