Stacked sequential scale-space taylor context

Carlo Gatta, Francesco Ciompi

    Research output: Contribution to journalArticleResearchpeer-review

    6 Citations (Scopus)


    We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. © 1979-2012 IEEE.
    Original languageEnglish
    Article number6701326
    Pages (from-to)1694-1700
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Issue number8
    Publication statusPublished - 1 Jan 2014


    • Contextual modeling
    • semantic image labeling
    • stacked sequential learning


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