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| Content Provider | ACM Digital Library |
|---|---|
| Author | Li, Jia |
| Abstract | A two-scale image retrieval system is developed to provide efficient search in large-scale databases as well as flexibility for users to incorporate ubjective preferences during retrieval. A new clustering method is developed for images each characterized by a varying number of weighted feature vectors. Furthermore, significant meta-information is mined within every cluster. A scanning mode of retrieval is created using cluster centers, which serve as a low scale version of a database in contrast to original images. In particular, users are presented with representative images of highly ranked clusters along with prominent meta-information. This retrieval approach enables users to quickly examine a large and diverse portion of a database surrounding a query and to learn about hidden connections between visual patterns and non-imagery types of data. The clusters formed also facilitate fast search in the case of individual image-based retrieval by filtering out images whose cluster centers are far from the query. The two-scale retrieval system has been implemented on a fine art painting database. Advantages of the system have been demonstrated by quantitative evaluation of the retrieval performance. |
| Starting Page | 499 |
| Ending Page | 502 |
| Page Count | 4 |
| File Format | |
| ISBN | 1595930442 |
| DOI | 10.1145/1101149.1101261 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2005-11-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Image retrieval Fast search Image clustering Meta-information feedback |
| Content Type | Text |
| Resource Type | Article |
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