Multi-hypothesis contextual modeling for semantic segmentation
Yükleniyor...
Dosyalar
Tarih
2019-01-01
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Science BV
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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
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
