Content based image retrieval through object features
Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Articulo Revision |
| Lenguaje: | Inglés |
| Publicado: |
2014
|
| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/41814 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct14-TO1.pdf |
| Aporte de: |
| Sumario: | Digital images are an increasingly important class of data, especially as computers become more usable with greater memory and communication capacities. As the demand for digital images increases, the need to store and retrieve images in an intuitive and efficient manner arises. These approaches can roughly be classified into three categories such as text-based, content-based and semantic based.
ARC-BC or convexity measures. The aim of this thesis to show that the rate of retrieval can be improved by combining various features than using a single characteristic. The proposed method combines colour, texture and geometric features to form a multidimension feature vector.
<i>(Párrafo extraído del texto a modo de resumen)</i> |
|---|