计算机工程2011，Vol.37Issue(21)：196-198,3.DOI:10.3969/j.issn.1000-3428.2011.21.067
基于多特征和改进SVM集成的图像分类
Image Classification Based on Multi-feature and Improved SVM Ensemble
付燕 1鲜艳明1
作者信息
- 1. 西安科技大学计算机学院,西安710054
- 折叠
摘要
Abstract
Aiming to the problem with poor classification accuracy of present image classification methods because they fail to apply fully complementary advantages between various single features of images and redundant information exists in the extracted features, this paper presents an image classification method based on multi-feature and improved Support Vector Machine(SVM) ensemble algorithm. Comprehensive features describing fully image content are extracted; redundant information is removed by transforming extracted features with Principal Component Analysis(PCA). RBaggSVM classifier is applied for classification. Simulation experimental result shows that this method has higher accuracy and faster speed of image classification than similar methods.关键词
多特征/主成分分析/支持向量机集成/PCA-RBaggSVM算法/图像分类Key words
multi-feature/Principal Component Analysis(PCA)/Support Vector Machine(SVM) ensemble/PCA-RBaggSVM algorithm/image classification分类
信息技术与安全科学引用本文复制引用
付燕,鲜艳明..基于多特征和改进SVM集成的图像分类[J].计算机工程,2011,37(21):196-198,3.
