Automatic detection of compound structures by joint selection of region groups from multiple hierarchical segmentations
| buir.advisor | Aksoy, Selim | |
| dc.contributor.author | Akçay, Hüseyin Gökhan | |
| dc.date.accessioned | 2016-09-30T06:19:36Z | |
| dc.date.available | 2016-09-30T06:19:36Z | |
| dc.date.copyright | 2016-09 | |
| dc.date.issued | 2016-09 | |
| dc.date.submitted | 2016-09-28 | |
| dc.department | Department of Computer Engineering | en_US |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (Ph.D.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2016. | en_US |
| dc.description | Includes bibliographical references (leaves 111-116). | en_US |
| dc.description.abstract | A challenging problem in remote sensing image interpretation is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are comprised of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of unknown number of primitives appearing in different primitive object layers in large scenes. The modeling process starts with example structures, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is reduced to the selection of multiple subsets of candidate regions from multiple hierarchical segmentations corresponding to different primitive object layers where each set of selected regions constitutes an instance of the example compound structures. The combinatorial selection problem is solved by joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements under the model learned from the example structures of interest. Moreover, we incorporate linear equality and inequality constraints on the candidate regions to prevent the co-selection of redundant overlapping regions and to enforce a particular spatial layout that must be respected by the selected regions. The constrained selection problem is formulated as a linearly constrained quadratic program that is solved via a variant of the primal-dual algorithm called the Difference of Convex algorithm by rewriting the non-convex program as the difference of two convex programs. Extensive experiments using very high spatial resolution images show that the proposed method can provide good localization of unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone. | en_US |
| dc.description.degree | Ph.D. | en_US |
| dc.description.statementofresponsibility | by Hüseyin Gökhan Akçay. | en_US |
| dc.format.extent | xix, 116 leaves : illustrations (some color), charts. | en_US |
| dc.identifier.itemid | B154165 | |
| dc.identifier.uri | http://hdl.handle.net/11693/32323 | |
| dc.language.iso | English | en_US |
| dc.publisher | Bilkent University | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Object detection | en_US |
| dc.subject | Spatial relationships | en_US |
| dc.subject | Context modeling | en_US |
| dc.subject | Markov random field | en_US |
| dc.subject | Maximum entropy distribution | en_US |
| dc.subject | Gibbs sampling | en_US |
| dc.subject | Swendsen-Wang sampling | en_US |
| dc.subject | Quadratic programming | en_US |
| dc.subject | Primal-dual algorithm | en_US |
| dc.title | Automatic detection of compound structures by joint selection of region groups from multiple hierarchical segmentations | en_US |
| dc.title.alternative | Bileşik yapıların çoklu sıradüzensel bölütlemelerden bölge gruplarının ortaklaşa seçilmesiyle otomatik sezimi | en_US |
| dc.type | Thesis | en_US |