| Data: |
2014 |
| Resum: |
Finding relations between image semantics and image characteristics is a problem of long standing in computer vision, image analysis or related fields. Classic research in these fields is intended for applications that go from the image domain to the semantic domain such as face recognition or scene understanding. This thesis explores methods and applications that go the opposite direction, i. e. use existing semantic information to infer knowledge and actions in the image domain. We build a large scale statistical framework to relate image characteristics to semantic expressions for millions of images and thousands of keywords. We apply the framework to semantic image enehancement and automatic color naming. |
| Nota: |
Advisor: Professor Sabine Süsstrunk. Date and location of PhD thesis defense: 1 March 2013, Ecole Polytechnique Fédérale de Lausanne. |
| Drets: |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Llengua: |
Anglès |
| Document: |
Altres ; recerca ; Versió publicada |
| Matèria: |
Features and Image Descriptors ;
Scene Understanding ;
Machine Learning and Data Mining ;
Image and Video Processing ;
Statistical and non linear methods ;
Semantics |
| Publicat a: |
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 11-12, ISSN 1577-5097 |
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