Higher Order Energies for Image Segmentation

Jianbing Shen*, Jianteng Peng, Xingping Dong, Ling Shao, Fatih Porikli

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    72 Citations (Scopus)

    Abstract

    A novel energy minimization method for general higher order binary energy functions is proposed in this paper. We first relax a discrete higher order function to a continuous one, and use the Taylor expansion to obtain an approximate lower order function, which is optimized by the quadratic pseudo-Boolean optimization or other discrete optimizers. The minimum solution of this lower order function is then used as a new local point, where we expand the original higher order energy function again. Our algorithm does not restrict to any specific form of the higher order binary function or bring in extra auxiliary variables. For concreteness, we show an application of segmentation with the appearance entropy, which is efficiently solved by our method. Experimental results demonstrate that our method outperforms the state-of-the-art methods.

    Original languageEnglish
    Article number7967759
    Pages (from-to)4911-4922
    Number of pages12
    JournalIEEE Transactions on Image Processing
    Volume26
    Issue number10
    DOIs
    Publication statusPublished - Oct 2017

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