DocumentCode :
1909972
Title :
Semantic Image Segmentation Based on Spatial Context Relations
Author :
Chang-Yong Ri ; Min Yao
Author_Institution :
Coll. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
fYear :
2012
fDate :
14-16 Dec. 2012
Firstpage :
104
Lastpage :
108
Abstract :
In this paper, the semantic image segmentation framework based on spatial context relations is proposed. First, the knowledge representation scheme of an image is introduced, which include fuzzy ontology structure and spatial context relations. For the purpose of the initial labeling of segmented regions, the multiclass fuzzy Support Vector Machine (multi-FSVM) is employed. A new image segmentation algorithm with high-level semantics is proposed, which is based on spatial context relations. At last, the experimental results are illustrated, and the advantage of the proposed image segmentation method is discussed.
Keywords :
fuzzy set theory; image segmentation; ontologies (artificial intelligence); support vector machines; fuzzy ontology structure; high-level semantics; image segmentation algorithm; knowledge representation scheme; multi-FSVM; multiclass fuzzy support vector machine; segmented region labeling; semantic image segmentation; spatial context relation; fuzzy ontology; multi-FSVM; semantic image segmentation; spatial context relations;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Engineering (ISISE), 2012 International Symposium on
Conference_Location :
Shanghai
ISSN :
2160-1283
Print_ISBN :
978-1-4673-5680-0
Type :
conf
DOI :
10.1109/ISISE.2012.31
Filename :
6495307
Link To Document :
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