Combined SIFT and bi-coherence features to detect image forgery
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Year
: 2014DOI: 10.1109/ChiCC.2014.6895730
Keyword(s): compressed sensing,n learning (artificial intelligence),n object tracking,n statistical analysis,n benchmark videos,n best object location,n drifting,n generative methods,n improved compressive tracking algorithm,n local context information,n local context learning,n low-level features,n object location likelihood function,n occlusion,n statistical correlation,n Computed tomography,n Context,n Context modeling,n Feature extraction,n Target
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Combined SIFT and bi-coherence features to detect image forgery
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| contributor author | Ju Zhang | |
| contributor author | Qiuqi Ruan | |
| contributor author | Yi Jin | |
| date accessioned | 2020-03-12T22:34:44Z | |
| date available | 2020-03-12T22:34:44Z | |
| date issued | 2014 | |
| identifier other | 7015314.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1087657 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Combined SIFT and bi-coherence features to detect image forgery | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8225531 | |
| subject keywords | compressed sensing | |
| subject keywords | n learning (artificial intelligence) | |
| subject keywords | n object tracking | |
| subject keywords | n statistical analysis | |
| subject keywords | n benchmark videos | |
| subject keywords | n best object location | |
| subject keywords | n drifting | |
| subject keywords | n generative methods | |
| subject keywords | n improved compressive tracking algorithm | |
| subject keywords | n local context information | |
| subject keywords | n local context learning | |
| subject keywords | n low-level features | |
| subject keywords | n object location likelihood function | |
| subject keywords | n occlusion | |
| subject keywords | n statistical correlation | |
| subject keywords | n Computed tomography | |
| subject keywords | n Context | |
| subject keywords | n Context modeling | |
| subject keywords | n Feature extraction | |
| subject keywords | n Target | |
| identifier doi | 10.1109/ChiCC.2014.6895730 | |
| journal title | ignal Processing (ICSP), 2014 12th International Conference on | |
| filesize | 788382 | |
| citations | 2 |