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CollageParsing: Nonparametric Scene Parsing by Adaptive Overlapping Windows
Frederick Tung and James J. Little
Department of Computer Science, University of British Columbia, Vancouver, CanadaAbstract. Scene parsing is the problem of assigning a semantic label to every pixel in an image. Though an ambitious task, impressive advances have been made in recent years, in particular in scalable nonparametric techniques suitable for open-universe databases. This paper presents the CollageParsing algorithm for scalable nonparametric scene parsing. In contrast to common practice in recent nonparametric approaches, CollageParsing reasons about mid-level windows that are designed to capture entire objects, instead of low-level superpixels that tend to fragment objects. On a standard benchmark consisting of outdoor scenes from the LabelMe database, CollageParsing achieves state-of-the-art nonparametric scene parsing results with 7 to 11% higher average per-class accuracy than recent nonparametric approaches.
Keywords: image parsing, semantic segmentation, scene understanding
LNCS 8694, p. 511 ff.
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© Springer International Publishing Switzerland 2014