A Trademark Retrieval Method Based on Support Vector Machines Self-learning

摘要：

Relevance feedback is a good method for semantic gap in image retrieval. In this paper we propose a method which uses support vector machines for conducting effective relevance feedback for trademark retrieval The algorithm takes the test results to adjust the already trained support vector machines. We select the Tamura textures features which consistent with human vision perception and the low-level feature of image to trian support vector machines. Experimental results show that it achieves significantly higher search accuracy after just three or four rounds of relevance feedback.

关键词： Content-based image retireval support vector machines relevance feedback Tamura texture feature

作者: Ya-Li Qi

作者单位: The Computer Department, Beijing Institute of Graphic Communication, Beijing, China, 102600

会议类型: 国际会议

会议名称: 2009 International Forum on Computer Science-Technology and Applications(2009年国际计算机科学技术与应用论坛 IFCSTA 2009)

会议地点: 重庆

会议语种:英文

页码: 681-684

在线出版日期: 2009-12-25（万方平台首次上网日期，不代表论文的发表时间）