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| Content Provider | IET Digital Library |
|---|---|
| Author | Yang, Xiaohui Cai, Lijun |
| Abstract | This study deals with the problem of similarity matching in region-based image retrieval (RBIR). A novel visual similarity measurement called adaptive region matching (ARM) has been developed. For decreasing negative influence of interference regions and important information loss simultaneously, a region importance index is constructed and semantic meaningful region (SMR) is introduced. Moreover, ARM automatically performs SMR-to-image matching or image-to-image matching. Extensive experiments on Corel-1000, Caltech-256 and University of Washington (UW) databases demonstrate the authors proposed ARM is more flexible and more efficient than the existing visual similarity measurements that were originally developed for RBIR. |
| Starting Page | 141 |
| Ending Page | 151 |
| Page Count | 11 |
| ISSN | 17519632 |
| Volume Number | 8 |
| e-ISSN | 17519640 |
| Issue Number | Issue 2, Apr (2014) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/8/2 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2012.0157 |
| Journal | IET Computer Vision |
| Publisher Date | 2013-10-18 |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Adaptive Region Matching ARM Caltech-256 Database Computer Vision And Image Processing Technique Corel-1000 Database Image Matching Image Retrieval Image-to-image Matching Information Retrieval Technique Interference Interference Region RBIR Region Importance Index Region-based Image Retrieval Semantic Meaningful Region Similarity Matching Problem SMR-to-image Matching Spatial And Pictorial Database UW Database Visual Database Visual Similarity Measurement |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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