Detection of Compound Structures Using Hierarchical Clustering of Statistical and Structural Features H. G ¨ okhan Akc ¸ ay Selim Aksoy Department of Computer Engineering Bilkent University Bilkent, 06800, Ankara, Turkey {akcay,saksoy}@cs.bilkent.edu.tr IGARSS 2011 IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 1 / 23
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Detection of Compound Structures UsingHierarchical Clustering of
Statistical and Structural Features
H. Gokhan Akcay Selim Aksoy
Department of Computer EngineeringBilkent University
Bilkent, 06800, Ankara, Turkey{akcay,saksoy}@cs.bilkent.edu.tr
I Compound structures are comprised of spatialarrangements of primitive objects such as buildings, roads,and trees.
I Our aim is to model compound structures in a hierarchicalsegmentation framework that can be used for semanticclassification, annotation, indexing, and retrievalapplications.
I The set of primitives includes objects that can be relativelyeasily extracted using low-level operations that exploitspectral, textural, and morphological information.
I The buildings that are used as primitive objects in theexperiments are detected using thresholding of differentspectral bands.
I A group of three or more objects are accepted as aligned iftheir centroids lie on a single straight line.−→ Least-squares line fitting on the centroid locations.
I Another important factor in an alignment is the uniformity ofthe spacing among the objects in the group.−→ Standard deviation of the distances between the centroids.
I After each vertex is assigned statistical and structuralfeatures, the next step is to group these objects using aniterative multi-level hierarchical clustering.
I The pairwise object distances in hierarchical clustering canbe computed separately for statistical features andstructural features.
I The distance for two objects with respect to statisticalfeatures is computed using sum of squared differencesbetween the corresponding features of these objects.
I The distance with respect to structural features is computedfrom the alignment groups that these objects belong to.
I The distance between two alignment groups is computedas the sum of squared differences between thecorresponding features of these groups.
I The distance for two objects is computed as the minimum ofthe distances between all pairs of alignment groups whereone group in a pair is associated with one of the objects andthe other group is associated with the other object.
I If at least one of the objects is not found to belong to anyalignment group, the distance of that object to any otherobject is set to∞.
I Once the statistical and structural distances are computedfor each neighboring object pair, agglomerative hierarchicalclustering iteratively groups these objects.
I We combine the results of separate clusterings usingstatistical distances and structural distances.
I The two clustering results are combined by assigning theobjects the same label if they are determined to belong tothe same cluster by either statistical distances or structuraldistances.
I Clustering using statistical distances resulted in 37 groupsof a total of 254 buildings.
I Clustering using structural distances resulted in 80 alignedgroups of a total of 402 buildings.
I The combined clustering produced 38 groups containing atotal of 403 buildings.
I We can conclude that groups of buildings with differentcharacteristics and spatial layouts that cannot be obtainedby traditional segmentation methods are successfullyextracted by the proposed method.
Figure 7: Example clustering results. Different groups are shown in differentcolors. The buildings that did not merge to any group at the given level areshown as white.