Browsing by Subject "Compound structures"

Now showing 1 - 6 of 6

Open Access
Detection of compound structures using clustering of statistical and structural features
(IEEE, 2012) Akçay, H. Gökhan; Aksoy, Selim
We describe a new method for detecting compound structures in images by combining the statistical and structural characteristics of simple primitive objects. A graph is constructed by assigning the primitive objects to its vertices, and connecting potentially related objects using edges. Statistical information that is modeled using spectral, shape, and position data of individual objects as well as the structural information that is modeled in terms of spatial alignments of neighboring object groups are also encoded in this graph. Experiments using WorldView-2 data show that hierarchical clustering of the graph vertices can discover high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE.
Open Access
Detection of compound structures using hierarchical clustering of statistical and structural features
(IEEE, 2011) Akçay, H. Gokhan; Aksoy, Selim
We describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE.
Open Access
Detection of compound structures using multiple hierarchical segmentations
(IEEE, 2012) Akçay, H. Gökhan; Aksoy, Selim
In this paper, our aim is to discover compound structures comprised of regions obtained from hierarchical segmentations of multiple spectral bands. A region adjacency graph is constructed by representing regions as vertices and connecting these vertices that are spatially close by edges. Then, dissimilarities between neighboring vertices are computed using statistical and structural features, and are assigned as edge weights. Finally, the compound structures are detected by extracting the connected components of the graph whose edges with relatively large weights are removed. Experiments using WorldView-2 images show that grouping of these vertices according to different criteria can extract high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE.
Open Access
Detection of compound structures using multiple hierarchical segmentations
(IEEE, 2014) Akçay, Hüseyin Gökhan; Aksoy, Selim
In this paper, we present a method for automatic compound structure detection in high-resolution images. Given a query compound structure, our aim is to detect coherent regions with similar spatial arrangement and characteristics in multiple hierarchical segmentations. A Markov random field is constructed by representing query 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 selection of similar region processes among a set of region hierarchies is achieved by maximizing the query model. Experiments using WorldView-2 images show the efficiency of probabilistic modeling of compound structures. © 2014 IEEE.
Open Access
Finding compound structures in images using image segmentation and graph-based knowledge discovery
(IEEE, 2009-07) Zamalieva, Daniya; Aksoy, Selim; Tilton J. C.
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.
Open Access
A spatial data model for remote sensing image retrieval
(IEEE, 2013) Akçay, H. Gökhan; Aksoy, Selim
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.