DocumentCode :
1879153
Title :
Boosting image segmentation
Author :
Koo, Hyung Il ; Cho, Nam Ik
Author_Institution :
Sch. of Electr. Eng., Seoul Nat. Univ., Seoul
fYear :
2008
fDate :
12-15 Oct. 2008
Firstpage :
3192
Lastpage :
3195
Abstract :
This paper presents a new approach to image segmentation, based on the conditional random fields (CRF) modeling and AdaBoost. In the proposed segmentation algorithm, the discriminating characteristics are first learned online using a training machine, and then the learnt characteristics are used to improve the region segmentation. The proposed algorithm is devised to include any kind of features even if they have different semantics, and to learn the difference of regions by selecting and combining only a few discriminating features among them. These novel properties are accomplished by a new Gibbs energy derived from CRF, AdaBoost, and probabilistic interpretation of its strong classifier. Experimental results on various images show the effectiveness of the proposed method.
Keywords :
free energy; image classification; image segmentation; learning (artificial intelligence); probability; AdaBoost; CRF modeling; Gibbs energy; conditional random fields modeling; image classifier; image segmentation; probabilistic interpretation; training machine; Algorithm design and analysis; Boosting; Computer vision; Design methodology; Image segmentation; Machine learning; Machine vision; Minimization methods; Power generation; Statistical distributions; AdaBoost; CRF; Image Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1765-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2008.4712474
Filename :
4712474
Link To Document :
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