Comparing Dissimilarity Measures for Content-Based Image Retrieval
Dissimilarity measurement plays a crucial role in contentbased image retrieval,where data objects and queries are represented as vectors in high-dimensional content feature spaces.Given the large number of dissimilarity measures that exist in many fields,a crucial research question arises: Is there a dependency,if yes,what is the dependency,of a dissimilarity measures retrieval performance,on different feature spaces? In this paper,we summarize fourteen core dissimilarity measures and classify them into three categories.A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection.From our experimental results,we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval,which will lay a foundation for developing more effective image search technologies.
dissimilarity measure feature space content-based image retrieval
Haiming Liu Dawei Song Stefan R(u)ger Rui Hu Victoria Uren
Knowledge Media Institute The Open University,Walton Hall Milton Kcynes,MK7 6AA,UK
国际会议
4th Asia Information Retrieval Symposium(AIRS 2008)(第四届亚洲信息检索研讨会)
哈尔滨
英文
44-50
2008-01-16（万方平台首次上网日期，不代表论文的发表时间）