Enhanced figure-ground classification with background prior propagation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Article number7004799
Pages (from-to)873-885
Journal / PublicationIEEE Transactions on Image Processing
Volume24
Issue number3
Online published8 Jan 2015
Publication statusPublished - Mar 2015

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Abstract

We present an adaptive figure-ground segmentation algorithm that is capable of extracting foreground objects in a generic environment. Starting from an interactively assigned background mask, an initial background prior is defined and multiple soft-label partitions are generated from different foreground priors by progressive patch merging. These partitions are fused to produce a foreground probability map. The probability map is then binarized via threshold sweeping to create multiple hard-label candidates. A set of segmentation hypotheses is formed using different evaluation scores. From this set, the hypothesis with maximal local stability is propagated as the new background prior, and the segmentation process is repeated until convergence. Similarity voting is used to select a winner set, and the corresponding hypotheses are fused to yield the final segmentation result. Experiments indicate that our method performs at or above the current state-of-the-art on several data sets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds.

Research Area(s)

  • Image segmentation, multiple hypotheses fusion, similarity voting

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