| 基于多特征和支持向量机的风景图像分类 |
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| 引用本文： | 周云蕾,郭洁畅,朱蓉,林青青,金小菲.基于多特征和支持向量机的风景图像分类[J].计算机系统应用,2016,25(5):135-141. |
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| 作者姓名： | 周云蕾 郭洁畅 朱蓉 林青青 金小菲 |
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| 作者单位： | 嘉兴学院 数理与信息工程学院, 嘉兴314001,杭州电子科技大学 数字媒体与艺术设计学院, 杭州 310018,嘉兴学院 数理与信息工程学院, 嘉兴314001,嘉兴学院 数理与信息工程学院, 嘉兴314001,嘉兴学院 数理与信息工程学院, 嘉兴314001 |
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| 基金项目： | 浙江省自然基金项目(LY15F020039);浙江省大学生科技创新活动暨新苗人才计划(2015R417026) |
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| 摘 要： | 本文提出了一种基于多特征和支持向量机的风景图像分类方法.首先,通过深入分析风景图像在视觉内容上的显著特点,利用融合颜色、纹理和形状等多种特征的方式来描述图像;其次,采用一种加权主成分方法对提取的高维图像特征进行有效降维;最后,运用基于支持向量机的分类器对图像进行分类.经试验验证,本文中提出的方法对风景图像有较好的分类效果.
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| 关 键 词： | 图像分类 特征提取 多特征 加权主成分分析 支持向量机 |
| 收稿时间： | 9/1/2015 12:00:00 AM |
| 修稿时间： | 2015/11/2 0:00:00 |
Landscape Image Classification Based on Multi-Feature Extraction and SVM Classifier |
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| ZHOU Yun-Lei,GUO Jie-Chang,ZHU Rong,LIN Qing-Qing and JIN Xiao-Fei.Landscape Image Classification Based on Multi-Feature Extraction and SVM Classifier[J].Computer Systems& Applications,2016,25(5):135-141. |
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| Authors: | ZHOU Yun-Lei GUO Jie-Chang ZHU Rong LIN Qing-Qing and JIN Xiao-Fei |
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| Affiliation: | College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China,School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China,College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China,College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China and College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China |
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| Abstract: | This paper mainly proposed a classification method based on multi features and support vector machine. Firstly, by analyzing the features of landscape image in the visual content, the image is described by means of fusion color, texture and shape features. Secondly, a weighted principal component method is used to reduce the features of high dimensional image. Finally, the experimental results show that the method proposed in this paper has a good classification effect on landscape images. |
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| Keywords: | image classification feature extraction multi features weighted principal component analysis(WPCA) support vector machine(SVM) |
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