A spatial data model for remote sensing image retrieval
Gökhan Akçay H.
2013 21st Signal Processing and Communications Applications Conference, SIU 2013
Item Usage Stats
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27995
Given a query region, our aim is to discover and retrieve regions with similar spatial arrangement and characteristics in other areas of the same large image or in other images. A Markov random field is constructed by representing regions as variables and connecting the vertices that are spatially close by edges. Then, a maximum entropy distribution is assumed over the query region process and retrieval of the similar region processes on the target image is achieved according to their probability. Experiments using WorldView-2 images show that statistical modelling of compound structures enable high-level and large-scale retrieval applications. © 2013 IEEE.
Markov random field
Markov Random Fields
Maximum entropy distribution
Remote sensing image retrieval
Spatial data model
Showing items related by title, author, creator and subject.
Çavuş Ö.; Aksoy, S. (2008)Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. In this study, a content-based image retrieval system that is based on scene ...
Gürkök H.; Karamuftuoglu, M.; Schaal, M. (2008)Traditionally, information retrieval systems rank documents according to the query terms they contain. However, even if a document may contain all query terms, this does not guarantee that it is relevant to the query. The ...
Sener F.; Ikizler-Cinbis, N. (Elsevier Ltd, 2014)Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ...