Name
Abstract | ||
|---|---|---|
Content-based image retrieval in facial image collec- tions is required in numerous applications. An interac- tive facial image retrieval method based on Self-Organizing Maps (SOM) is presented in this paper, in which multiple features are involved in the queries simultaneously. In addi- tion, the retrieval performance is improved not only within queries for current user but also between queries by long- term learning from other users' relevance feedback. In that way recorded human intelligence is integrated to the system as a statistical feature. The work constituting this paper has been incorporated into our image retrieval system named PicSOM. The results of evaluation experiments show that the query performance can be substantially increased by using multiple features and the long-term learning. |
Year | Venue | Keywords |
|---|---|---|
2005 | MVA | machine vision,image retrieval |
Field | DocType | Citations |
Computer vision,Automatic image annotation,Relevance feedback,Information retrieval,Computer science,Human intelligence,Image retrieval,Self-organizing map,Artificial intelligence,Biometrics,Content-based image retrieval,Visual Word | Conference | 10 |
PageRank | References | Authors |
0.63 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhirong Yang | 1 | 289 | 17.27 |
| Jorma Laaksonen | 2 | 1162 | 176.93 |