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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jie Xiao Yun Fu Yijuan Lu Qi Tian |
| Copyright Year | 2009 |
| Description | Author affiliation: BBN Technologies, Cambridge, MA 02138, USA (Yun Fu) || Dept. of Computer Science, University of Texas at San Antonio, 78249, USA (Jie Xiao; Qi Tian) || Dept. of Computer Science, Texas State University, San Marcos, 78747, USA (Yijuan Lu) |
| Abstract | Can we take advantage of the huge number of online images to improve image search quality? Motivated by this question, we propose a novel model to re-rank Google image search results by exploring the latent characteristic of massive unrelated images as a clue to filter them in the reranking. Inspired by the characteristic of the intrinsic diversity and the unwanted availability of the unrelated images, in our model, we adopt one-class classification to build a hyper-sphere for the target objects, unrelated images, and construct a robust boundary to distinguish them from the related images effectively. Then the Google results can be easily re-ranked by filtering the unrelated images with the built-up model. Extensive experiments demonstrate our approach outperforms Google image search engine's results, even if its baseline is high. |
| Starting Page | 314 |
| Ending Page | 317 |
| File Size | 308846 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424442904 |
| ISSN | 19457871 |
| DOI | 10.1109/ICME.2009.5202498 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image retrieval Filtering Feedback Keyword search Image representation Computer science Filters Search engines Robustness Web search image retrieval One-class classification re-ranking relevance feedback |
| Content Type | Text |
| Resource Type | Article |
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