Large-scale Satellite Image Browsing using Automatic Semantic Categorization
and Content-based Retrieval
Ashish Parulekar, Ritendra Datta, Jia Li, James Z. Wang
The Pennsylvania State University
We approach the problem of large-scale satellite image browsing from a
content-based retrieval and semantic categorization perspective. A
two-stage method for query based automatic retrieval of satellite
image patches is proposed. The semantic category of query patches are
determined and patches from that category are ranked based on an image
similarity measure. Semantic categorization is done by a learning
approach involving the two-dimensional multi-resolution hidden Markov
model (2-D MHMM). Patches that do not belong to any trained category
are handled using a support vector machine (SVM) based classifier.
Experiments yield promising results in modeling semantic categories
within satellite images using 2-D MHMM, producing accurate and
convenient browsing. We also show that prior semantic categorization
improves retrieval performance.
Full Paper in Color
Ashish Parulekar, Ritendra Datta, Jia Li and James Z. Wang,
``Large-scale Satellite Image Browsing using Automatic Semantic
Categorization and Content-based Retrieval,'' Proceedings of the IEEE
International Workshop on Semantic Knowledge in Computer Vision, in
conjunction with IEEE International Conference on Computer Vision, 8
pages, Beijing, China, IEEE, October 2005.
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Mon Aug 29 18:38:04 EDT 2005
© 2005, James Z. Wang