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  • Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
  • MF - Bildiri Koleksiyonu | Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering
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Improving semantic segmentation with generalized models of local context

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Date

2017

Author

Ateş, Hasan Fehmi
Sünetci, Sercan

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Citation

Ates, H. F., & Sunetci, S. (2017). Improving semantic segmentation with generalized models of local context. Paper presented at the , 10425 320-330. doi:10.1007/978-3-319-64698-5_27

Abstract

Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Superpixel image parsing methods provide this consistency by carrying out labeling at the superpixel-level based on superpixel features and neighborhood information. In this paper, we develop generalized and flexible contextual models for superpixel neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models to combine complementary information available in alternative superpixel segmentations of the same image. Simulation results on two datasets demonstrate significant improvement in parsing accuracy over the baseline approach.

Volume

10425

URI

http://dx.doi.org/10.1007/978-3-319-64698-5_27
https://hdl.handle.net/11729/1507

Collections

  • Bildiri Koleksiyonu [231]
  • Bildiri Koleksiyonu [311]
  • MF - Bildiri Koleksiyonu | Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical-Electronics Engineering [184]



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