|Table of Contents|

Image segmentation application based on incremental learning vector SVM algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2014年01期
Page:
6-11
Research Field:
Publishing date:

Info

Title:
Image segmentation application based on incremental learning vector SVM algorithm
Author(s):
Chen Yuantao12Xu Weihong12Wu Jiaying12Hu Rong1
1.School of Computer Science and Engineering,NUST,Nanjing 210094,China; 2.School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410014,China
Keywords:
support vector machine incremental learning vector support vector machine image segmentation thin narrow set
PACS:
TP391
DOI:
-
Abstract:
In order to solve less efficient,longer time-consuming problem of the traditional SVM methods,this paper proposes a support vector machine learning algorithm based on the incremental vector.The algorithm obtains the initial support vector machine classifier by training the sample collection incremental vector learning.This paper streamlines the relevant conditions for initial training sample set to be streamlined narrow set by using the initialization classification,applies the thin narrow set of initial support vector machine classifier in reverse processing,and gets the support vector machine classification devices.The algorithm can significantly reduce the training time of support vector machine and large-capacity data set and has good generalization performance.In order to verify the application of the algorithm,this paper selects relative color images from Berkeley image segmentation data set BSDS500 and Internet experiments to do simulation experiment.The experimental results show that this segmentation process has much less time-consuming than the traditional support vector machine and better segmentation than the manually marked results in Berkeley image segmentation dataset.

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Memo

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Last Update: 2014-02-28