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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jianping Fan Yi Shen Ning Zhou Yuli Gao |
| Copyright Year | 2010 |
| Description | Author affiliation: Multimedia Interaction and Understanding, HP Labs, Palo Alto, CA94304, USA (Yuli Gao) || Department of Computer Science, UNC-Charlotte, NC28223, USA (Jianping Fan; Yi Shen; Ning Zhou) |
| Abstract | To leverage large-scale weakly-tagged images for computer vision tasks (such as object detection and scene recognition), a novel cross-modal tag cleansing and junk image filtering algorithm is developed for cleansing the weakly-tagged images and their social tags (i.e., removing irrelevant images and finding the most relevant tags for each image) by integrating both the visual similarity contexts between the images and the semantic similarity contexts between their tags. Our algorithm can address the issues of spams, polysemes and synonyms more effectively and determine the relevance between the images and their social tags more precisely, thus it can allow us to create large amounts of training images with more reliable labels by harvesting from large-scale weakly-tagged images, which can further be used to achieve more effective classifier training for many computer vision tasks. |
| Starting Page | 802 |
| Ending Page | 809 |
| File Size | 1618664 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540135 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Large-scale systems Image databases Computer vision Collaboration Layout Tagging Object detection Image recognition Internet Large scale integration |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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