DocumentCode :
557733
Title :
Boundary detection method based on supervising for small sample size problem
Author :
Gao, Liang ; Liu, Xiaoyun
Author_Institution :
Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
Volume :
3
fYear :
2011
fDate :
15-17 Oct. 2011
Firstpage :
1218
Lastpage :
1222
Abstract :
In this paper, we address segmentation of the image with gray and texture measurements together. Combining the filter banks and improved K-Means clustering, the texton is extracted effectively in small samples case. And then, a model used for boundary detection is proposed. This model combines multiple cues, such as gray and texture feature. Proposed model trains parameters using human labeled images and therefore the output of trained model is detected boundary. Finally, we optimize the extracted boundary. The results show that our method not only can accurately detect the boundary but also reduce the time complexity in small samples case compared to the existing method.
Keywords :
edge detection; feature extraction; filtering theory; image colour analysis; image segmentation; image texture; pattern clustering; K-means clustering; boundary detection; boundary extraction; filter bank; gray feature; gray measurement; human labeled image; image segmentation; small sample size problem; texton; texture feature; texture measurement; time complexity; Clustering algorithms; Feature extraction; Humans; Image color analysis; Image edge detection; Image segmentation; Training; boundary detection; improved K-Means clustering; small sample size problem; supervised learning; texton feature extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
Type :
conf
DOI :
10.1109/CISP.2011.6100403
Filename :
6100403
Link To Document :
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