Name
Abstract | ||
|---|---|---|
Dissimilarity measurement plays a crucial role in content-based 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 measure's 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. |
Year | Venue | Keywords |
|---|---|---|
2008 | AIRS | dissimilarity measurement,effective image search technology,retrieval performance,high-dimensional content feature space,dissimilarity measure,crucial research question,content-based image retrieval,corel image collection,fourteen core dissimilarity measure,different feature space,feature space |
DocType | Volume | ISSN |
Conference | 4993 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-68633-9 | 48 | 1.89 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiming Liu | 1 | 116 | 14.85 |
| Dawei Song | 2 | 144 | 9.98 |
| Stefan Rüger | 3 | 621 | 45.08 |
| Rui Hu | 4 | 85 | 5.46 |
| Victoria Uren | 5 | 1184 | 78.67 |