| 000 | 01508cam a2200325 a 4500 | ||
|---|---|---|---|
| 003 | EG-GiCUC | ||
| 005 | 20250223030731.0 | ||
| 008 | 130101s2012 ua dh f m 000 0 eng d | ||
| 040 |
_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.20.01.M.Sc.2012.Mo.A | ||
| 100 | 0 | _aMohamed Sami Ismail Abdulhammed | |
| 245 | 1 | 0 |
_aAutomatic image annotation / _cMohamed Sami Ismail Abdulhammed ; Supervised AboulElla Hassanien |
| 246 | 1 | 5 | _aالإستدلال التلقائي للصور |
| 260 |
_aCairo : _bMohamed Sami Ismail Abdulhammed , _c2012 |
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| 300 |
_a79 Leaves : _bcharts , facsimiles ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Information Technology | ||
| 520 | _aThis thesis presents four image annotation approaches. The first two presented approaches are based on the optimization of feature weighting for each class using genetic algorithm and particle swarm optimization techniques. For classification and annotation, a multi-calss support vector machine and k-means with key nearest nighbor machine learning classifiers have been applied | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aAutomatic image annotation | |
| 653 | 4 | _aImage retrieval | |
| 653 | 4 | _aOptimization | |
| 700 | 0 |
_aAboulella Afifi Hassanien , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aSamia _eCataloger |
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| 942 |
_2ddc _cTH |
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| 999 |
_c40824 _d40824 |
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