DocumentCode
47218
Title
Stacked Sequential Scale-SpaceTaylor Context
Author
Gatta, Carlo ; Ciompi, Francesco
Author_Institution
Centre de Visio per Computador, Bellaterra, Spain
Volume
36
Issue
8
fYear
2014
fDate
Aug. 2014
Firstpage
1694
Lastpage
1700
Abstract
We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
Keywords
feature extraction; image representation; image segmentation; learning (artificial intelligence); CAMVID data set; KAIST2 data set; MSRC-21 data set; contextual information; eTRIMS8 data set; local Taylor coefficients extraction; posterior label; sequential image labeling methods; stacked sequential scale-space Taylor context; Context; Feature extraction; Image segmentation; Nickel; Semantics; Training; Vectors; Contextual modeling; semantic image labeling; stacked sequential learning;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2013.2297706
Filename
6701326
Link To Document