DocumentCode
1858008
Title
Notice of Retraction
Global Feature-Based Image Classification and Recognition in Small Sample Size Problem
Author
Lan Gan ; Zhongping Yu ; Jie Gao
Author_Institution
Sch. of Inf. Eng., East China Jiaotong Univ., Nanchang, China
fYear
2010
fDate
22-24 Jan. 2010
Firstpage
30
Lastpage
33
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we designs an image classification and recognition systems. With a series approaches of image pre-processing, segmentation and tracking, extract the global image characteristic parameters ( the characteristics of the whole picture). According to obtain the characteristic parameters to realize the image classification and identification. Since this article mainly related to small sample data, so the use of nuclear multi-feature linear discriminant analysis (kernel multi-feature FLDA, short kMFLDA), KNN method and support vector machine (SVM) Comparison of three methods of classification and recognition rate, experimental results show that the support vector machine (SVM) classification recognition rate higher, more reliable.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we designs an image classification and recognition systems. With a series approaches of image pre-processing, segmentation and tracking, extract the global image characteristic parameters ( the characteristics of the whole picture). According to obtain the characteristic parameters to realize the image classification and identification. Since this article mainly related to small sample data, so the use of nuclear multi-feature linear discriminant analysis (kernel multi-feature FLDA, short kMFLDA), KNN method and support vector machine (SVM) Comparison of three methods of classification and recognition rate, experimental results show that the support vector machine (SVM) classification recognition rate higher, more reliable.
Keywords
feature extraction; image classification; image recognition; image segmentation; support vector machines; tracking; KNN method; SVM; global feature-based image classification; image recognition; image segmentation; image tracking; kernel multi-feature FLDA; nuclear multifeature linear discriminant analysis; small sample size problem; support vector machine; Biomedical imaging; Feature extraction; Image classification; Image recognition; Image segmentation; Impurities; Linear discriminant analysis; Pixel; Support vector machine classification; Support vector machines; Global characteristics; KNN method; nuclear multi-feature linear discriminant analysis; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Education, e-Business, e-Management, and e-Learning, 2010. IC4E '10. International Conference on
Conference_Location
Sanya
Print_ISBN
978-1-4244-5680-2
Type
conf
DOI
10.1109/IC4E.2010.47
Filename
5432390
Link To Document