Multi-hypothesis contextual modeling for semantic segmentation

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Tarih

2019-01-01

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Elsevier Science BV

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Araştırma projeleri

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Dergi sayısı

Özet

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. Segmentation result is typically improved by Markov Random Field (MRF) optimization on the initial labels. However this improvement is limited by the accuracy of initial result and how the contextual neighborhood is defined. In this paper, we develop generalized and flexible contextual models for segmentation neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image. In other words, we propose a novel MRF framework that describes and optimizes the contextual dependencies between multiple segmentations. Simulation results on two common datasets demonstrate significant improvement in parsing accuracy over the baseline approaches.

Açıklama

Anahtar Kelimeler

Image parsing, Segmentation, Superpixel, MRF, Scene

Kaynak

Pattern Recognition Letters

WoS Q Değeri

Q2
Q2

Scopus Q Değeri

Q1

Cilt

117

Sayı

Künye

Ateş, H. F. & Sünetci, S. (2019). Multi-hypothesis contextual modeling for semantic segmentation. Pattern Recognition Letters, 117, 104-110. doi:10.1016/j.patrec.2018.12.011