Finding compound structures in images using image segmentation and graph-based knowledge discovery
Tilton J. C.
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2009
V252 - V255
Item Usage Stats
MetadataShow full item record
We present an unsupervised method for discovering compound image structures that are comprised of simpler primitive objects. An initial segmentation step produces image regions with homogeneous spectral content. Then, the segmentation is translated into a relational graph structure whose nodes correspond to the regions and the edges represent the relationships between these regions. We assume that the region objects that appear together frequently can be considered as strongly related. This relation is modeled using the transition frequencies between neighboring regions, and the significant relations are found as the modes of a probability distribution estimated using the features of these transitions. Experiments using an Ikonos image show that subgraphs found within the graph representing the whole image correspond to parts of different high-level compound structures. ©2009 IEEE.
Digital image storage
Published Version (Please cite this version)http://dx.doi.org/10.1109/IGARSS.2009.5417683
Showing items related by title, author, creator and subject.
Akbudak K.; Aykanat, C. (IEEE Computer Society, 2017)Exploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph ...
Dogrusoz, U.; Belviranli, M. E.; Dilek, A. (Institute of Electrical and Electronics Engineers, 2013)We present a new algorithm for automatic layout of clustered graphs using a circular style. The algorithm tries to determine optimal location and orientation of individual clusters intrinsically within a modified spring ...
Dogrusoz, U. (Elsevier, 2002)We present and contrast several efficient two-dimensional packing algorithms for specified aspect ratio. These near-linear algorithms are based on strip packing, tiling, and alternate-bisection methodologies and can be ...