Detection of Compound Structures Using              Hierarchical Clustering of      Statistical and Structural Features   ...
Introduction   A common approach for object recognition is to segment   the images into homogeneous regions.   An alternat...
Introduction   Compound structures are comprised of spatial   arrangements of primitive objects such as buildings, roads, ...
Outline1   Detection of primitive objects2   Feature extraction      Image representation      Statistical features      S...
Detection of primitive objects   The set of primitives includes objects that can be relatively   easily extracted using lo...
Detection of primitive objects                  (a) Ankara image                          (b) Detected buildingsFigure 1: ...
Feature extractionImage representation     The image content is modeled using attributed relational     graphs where the p...
Feature extractionImage representation                  (a) Building mask                         (b) Neighborhood graphFi...
Feature extractionStatistical features      The statistical features that summarize the spectral content      and the shap...
Feature extractionStructural features      The structural features represent the spatial layout of each      object with r...
Feature extractionStructural features      A group of three or more objects are accepted as aligned if      their centroid...
Feature extractionStructural features      Given an image containing n objects, groups of aligned      ones can be found b...
Feature extractionStructural features      Once a path with at least three vertices is obtained, it is      considered for...
Feature extractionStructural features                   (a) Building mask                         (b) Alignment detectionF...
Feature extractionStructural features                   (a) Building mask                         (b) Alignment detectionF...
Feature extractionStructural features                   (a) Building mask                         (b) Alignment detectionF...
Grouping of primitive objects   After each vertex is assigned statistical and structural   features, the next step is to g...
Grouping of primitive objects   The distance with respect to structural features is computed   from the alignment groups t...
Grouping of primitive objects   Once the statistical and structural distances are computed   for each neighboring object p...
Experiments   We performed experiments on the WV-2 image of Ankara.   A graph was constructed with 418 vertices correspond...
Experiments   Clustering using statistical distances resulted in 37 groups   of a total of 254 buildings.   Clustering usi...
Experiments   (a) Statistical clustering        (b) Structural clustering                 (c) Combined clusteringFigure 7:...
Conclusions   We described a new procedure that combines          statistical characteristics of primitive objects modeled...
Upcoming SlideShare
Loading in …5
×

DetectionOfCompoundStructuresUsingHierarchicalClusteringOfStatisticalAndStructuralFeatures.pdf

297 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
297
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

DetectionOfCompoundStructuresUsingHierarchicalClusteringOfStatisticalAndStructuralFeatures.pdf

  1. 1. Detection of Compound Structures Using Hierarchical Clustering of Statistical and Structural Features ¨ H. Gokhan Akcay ¸ Selim Aksoy Department of Computer Engineering Bilkent University Bilkent, 06800, Ankara, Turkey {akcay,saksoy}@cs.bilkent.edu.tr IGARSS 2011IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 1 / 23
  2. 2. Introduction A common approach for object recognition is to segment the images into homogeneous regions. An alternative for semantic image understanding is to identify the image regions that are intrinsically heterogeneous. Examples of such regions, also called compound structures, include different types of residential, commercial, industrial, and agricultural areas. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 2 / 23
  3. 3. Introduction Compound structures are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. Our aim is to model compound structures in a hierarchical segmentation framework that can be used for semantic classification, annotation, indexing, and retrieval applications. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 3 / 23
  4. 4. Outline1 Detection of primitive objects2 Feature extraction Image representation Statistical features Structural features3 Grouping of primitive objects4 Experiments5 Conclusions IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 4 / 23
  5. 5. Detection of primitive objects The set of primitives includes objects that can be relatively easily extracted using low-level operations that exploit spectral, textural, and morphological information. The buildings that are used as primitive objects in the experiments are detected using thresholding of different spectral bands. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 5 / 23
  6. 6. Detection of primitive objects (a) Ankara image (b) Detected buildingsFigure 1: Examples of building detection in a WorldView-2 image of Ankara,Turkey. The detected buildings are highlighted with red in (b). IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 6 / 23
  7. 7. Feature extractionImage representation The image content is modeled using attributed relational graphs where the primitives form the vertices. We assume that neighboring objects can be related, and connect every neighboring vertex pair with an edge. We use a threshold on the distance between the centroids of object pairs to determine the neighbors. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 7 / 23
  8. 8. Feature extractionImage representation (a) Building mask (b) Neighborhood graphFigure 2: Examples of graph construction. The vertices considered asneighbors based on proximity analysis are connected with red edges in (b). IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 8 / 23
  9. 9. Feature extractionStatistical features The statistical features that summarize the spectral content and the shape of each individual object consist of mean values of the pixels within the object for each spectral band, area, eccentricity, and centroid location. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 9 / 23
  10. 10. Feature extractionStructural features The structural features represent the spatial layout of each object with respect to its neighbors. An important structural information is the amount of alignment among objects. We propose a new method for the detection of aligned object groups. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 10 / 23
  11. 11. Feature extractionStructural features A group of three or more objects are accepted as aligned if their centroids lie on a single straight line. −→ Least-squares line fitting on the centroid locations. Another important factor in an alignment is the uniformity of the spacing among the objects in the group. −→ Standard deviation of the distances between the centroids. Figure 3: Illustration of object alignment. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 11 / 23
  12. 12. Feature extractionStructural features Given an image containing n objects, groups of aligned ones can be found by checking all possible subsets having at least three objects. These subsets can be generated using a depth-first search on the neighborhood graph. Depth-first search starts at a particular vertex v, and recursively traverses all vertices connected to v. This procedure is repeated by starting the search algorithm from each vertex in the graph. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 12 / 23
  13. 13. Feature extractionStructural features Once a path with at least three vertices is obtained, it is considered for possible alignment. We perform pruning by using thresholds on the sum of squared residuals for line fitting and the standard deviation of centroid distances. At the end of the search procedure, the set of structural features computed for each object group consists of orientation of the fitted line, and mean of the centroid distances. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 13 / 23
  14. 14. Feature extractionStructural features (a) Building mask (b) Alignment detectionFigure 4: Examples of alignment detection. All groups of buildings satisfyingthe alignment criteria are shown in (b). IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 14 / 23
  15. 15. Feature extractionStructural features (a) Building mask (b) Alignment detectionFigure 5: Examples of alignment detection. A total of 622 aligned groups ofthree or more objects are detected but only 34 are shown for clarity. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 15 / 23
  16. 16. Feature extractionStructural features (a) Building mask (b) Alignment detectionFigure 6: Examples of alignment detection. All groups of buildings satisfyingthe alignment criteria are shown in (b). IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 16 / 23
  17. 17. Grouping of primitive objects After each vertex is assigned statistical and structural features, the next step is to group these objects using an iterative multi-level hierarchical clustering. The pairwise object distances in hierarchical clustering can be computed separately for statistical features and structural features. The distance for two objects with respect to statistical features is computed using sum of squared differences between the corresponding features of these objects. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 17 / 23
  18. 18. Grouping of primitive objects The distance with respect to structural features is computed from the alignment groups that these objects belong to. The distance between two alignment groups is computed as the sum of squared differences between the corresponding features of these groups. The distance for two objects is computed as the minimum of the distances between all pairs of alignment groups where one group in a pair is associated with one of the objects and the other group is associated with the other object. If at least one of the objects is not found to belong to any alignment group, the distance of that object to any other object is set to ∞. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 18 / 23
  19. 19. Grouping of primitive objects Once the statistical and structural distances are computed for each neighboring object pair, agglomerative hierarchical clustering iteratively groups these objects. We combine the results of separate clusterings using statistical distances and structural distances. The two clustering results are combined by assigning the objects the same label if they are determined to belong to the same cluster by either statistical distances or structural distances. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 19 / 23
  20. 20. Experiments We performed experiments on the WV-2 image of Ankara. A graph was constructed with 418 vertices corresponding to the detected buildings and 2, 610 edges obtained using proximity analysis. The alignment detection algorithm resulted in 622 groups of three or more buildings aligned within the allowed limits. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 20 / 23
  21. 21. Experiments Clustering using statistical distances resulted in 37 groups of a total of 254 buildings. Clustering using structural distances resulted in 80 aligned groups of a total of 402 buildings. The combined clustering produced 38 groups containing a total of 403 buildings. We can conclude that groups of buildings with different characteristics and spatial layouts that cannot be obtained by traditional segmentation methods are successfully extracted by the proposed method. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 21 / 23
  22. 22. Experiments (a) Statistical clustering (b) Structural clustering (c) Combined clusteringFigure 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. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 22 / 23
  23. 23. Conclusions We described a new procedure that combines statistical characteristics of primitive objects modeled using spectral, shape, and position information with structural characteristics encoded using their spatial alignments modeled in terms of joint orientation and uniformity of inter-object spacing for the detection of compound structures. Future work includes methods for automatic selection of parameters and experiments on larger data sets. IGARSS 2011 c 2011, Akcay and Aksoy (Bilkent University) 23 / 23

×