Name
Abstract | ||
|---|---|---|
In this paper, a learning method is proposed to improve the retrieval process in image databases. This method uses the search transaction logs in the system and user relevance feedback scores to create a semantic space of the image database. The semantic space includes many semantic classes and all the images in the database are clustered to these classes with different membership values. The sparsity problem in the transaction logs data is solved by filling the missing values by an estimation based on the image contents and image similarities. A Fuzzy clustering algorithm is developed to create the semantic classes and find image memberships in the classes. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1145/1027527.1027636 | ACM Multimedia 2001 |
Keywords | Field | DocType |
search transaction log,semantic space,image database,transaction logs data,image databases,users relevance feedback,fuzzy clustering algorithm,image content,semantic class,image membership,image similarity,image semantics,missing values,fuzzy clustering,image retrieval | Fuzzy clustering,Automatic image annotation,Relevance feedback,Information retrieval,Computer science,Image retrieval,Transaction log,Image database,Missing data,Semantics | Conference |
ISBN | Citations | PageRank |
1-58113-893-8 | 2 | 0.36 |
References | Authors | |
13 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amin Shah-hosseini | 1 | 6 | 0.77 |
| Gerald M. Knapp | 2 | 55 | 7.35 |