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| 基于SVM的图像分类 | |
| 引用本文： | 胡斌斌,姚明海. 基于SVM的图像分类[J]. 微计算机信息, 2010, 0(1): 115-116,156 |
| 作者姓名： | 胡斌斌 姚明海 |
| 作者单位： | 浙江工业大学信息学院,杭州310014 |
| 摘 要： | 现有的图像检索系统多是针对底层特征的系统,而人类往往习惯于在语义级别进行相似性判别。如何跨越底层特征和高层语义之间的＂鸿沟＂,成为基于内容检索的研究重点。本文提出一种利用SVM提取图像的高层特征,然后对图像进行语义级别的分类。实验结果表明,该方法在一定程度上跨越＂语义鸿沟＂。 |
| 关 键 词： | 支持向量机 分类器 底层特征 高层语义 |
Image Classification by Using SVM |
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| HU Bin-bin YAO Ming-hai. Image Classification by Using SVM[J]. Control & Automation, 2010, 0(1): 115-116,156 | |
| Authors: | HU Bin-bin YAO Ming-hai |
| Affiliation: | HU Bin-bin YAO Ming-hai (College of Information Engineering,Zhejiang University of Technology,Hangzhou Zhejiang 310014,China) |
| Abstract: | image retrieval systems are mostly based on low-features,but people are accustomed to judging on high-semantic level.It becomes more important that overcome the considerable gap between them.This paper uses SVM to get the high-semantic concepts and classify the testing images.The results indicate that the SVM can span this gap to some extent. |
| Keywords: | SVM classifier low-feature high-semantic |
| 本文献已被 CNKI 维普 等数据库收录！ | |