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基于优势集聚类和支持向量机的图像检索 被引量：2

Image Retrieval Based on Dominant Set Clustering and Support Vector Machine
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摘要 提出一种基于改进优势集聚类的无监督学习图像检索方法.使用有记忆的 SVM 相关反馈将底层视觉特征和高层语义相结合,并充分发掘图像之问的相似性以得到更接近用户检索要求的结果.实验结果表明,该方法能快速收敛于用户的查询概念,在图像检索系统的准确率和反馈次数方面表现出一定的优越性. An unsupervised learning approach for content based image retrieval system is presented, which combines the low-level vision feature and the high-level semantics using the memorized SVM relevance feedback. The proposed approach fully explores the similarities among images in database by using the improved dominant set clustering to optimize the ＂ relevance＂ feedback results from SVM. The experimental results show that the proposed method can be convergent to user＇s retrieval concept rapidly, and it has the superior precision and total relevance feedback times in image retrieval system.
作者 王曼 彭国华 叶正麟 赵丛 王树勋
机构地区 西北工业大学理学院应用数学系 陕西理工学院数学系
出处 《模式识别与人工智能》 EI CSCD 北大核心 2008年第5期689-694,共6页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.60672135) 陕西省教育厅科学研究计划项目(No.07JK209)资助
关键词 优势集聚类 支持向量机(SVM) 基于内容的图像检索 Dominant Set Clustering （DSC）, Support Vector Machine （SVM）, Content Based Image Retrieval （CBIR）
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献12

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共引文献32

同被引文献18

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模式识别与人工智能
2008年 第5期

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