DocumentCode :
1946178
Title :
Scene Classification Combining Low-level and Semantic Modeling Strategies
Author :
Zhou Li ; Hu Dewen
Author_Institution :
Nat. Univ. of Defense Technol., Changsha, China
fYear :
2011
fDate :
5-7 Aug. 2011
Firstpage :
1071
Lastpage :
1075
Abstract :
Scene classification is an important issue in the field of computer vision. Although considerable progress has been made, it remains a challenging issue. Most of the current scene classification approaches are based on either low-level or semantic modeling strategies. This paper presents a novel scene classification approach based on combining low-level and semantic modeling strategies. The experimental results show that the proposed approach performs competitively against previous methods across three publicly and commonly used data sets.
Keywords :
computer vision; image classification; natural scenes; computer vision; low-level modeling strategy; scene classification; semantic modeling strategy; Computational modeling; Computer vision; Feature extraction; Histograms; Semantics; Support vector machines; Training; SVM; bag-of-features; feature combination; low-level modeLing strategy; scene classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2011 Second International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4577-0755-1
Electronic_ISBN :
978-0-7695-4455-7
Type :
conf
DOI :
10.1109/ICDMA.2011.265
Filename :
6052156
Link To Document :
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