Name
Abstract | ||
|---|---|---|
The current trend of image retrieval is to incorporate image semantics with visual features to enhance retrieval performance. Although many approaches annotate images with keywords and process query at the semantic level, they fail to explore the full potentials of semantics. This paper proposes thesaurus-aided approaches to facilitate semantics-based access to images. The contribution of our work are two-fold: constructing a dynamic semantic hierarchy (DSH) which supports flexible image browsing by semantic subjects, as well as formulating a semantic similarity metric to improve the accuracy of semantic matching. Both approaches are seamlessly integrated into a unified framework for semantics- and feature-based image retrieval. Experiments conducted on the real-world images demonstrate the effectiveness of our approaches. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICME.2001.1237927 | ICME |
Keywords | Field | DocType |
semantic similarity,prototypes,computer science,feedback,availability,image retrieval,digital images | Semantic similarity,Computer vision,Automatic image annotation,Information retrieval,Semantic hierarchy,Computer science,Image retrieval,Digital image,Artificial intelligence,Content based retrieval,Semantics,Visual Word | Conference |
Citations | PageRank | References |
12 | 0.76 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 50 | 5.64 |
| Liu Wenyin | 2 | 2531 | 215.13 |
| Hong-Jiang ZHANG | 3 | 17378 | 1393.22 |
| Yue-Ting Zhuang | 4 | 3549 | 216.06 |