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Agglomerative connectivity constrained clustering for image segmentation
Author(s) -
Li Jia
Publication year - 2011
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10109
Subject(s) - cluster analysis , computer science , pattern recognition (psychology) , artificial intelligence , single linkage clustering , hierarchical clustering , correlation clustering , image segmentation , data mining , segmentation , cure data clustering algorithm , canopy clustering algorithm , segmentation based object categorization , scale space segmentation
We consider the problem of clustering under the constraint that data points in the same cluster are connected according to a pre‐existed graph. This constraint can be efficiently addressed by an agglomerative clustering approach, which we exploit to construct a new fully automatic segmentation algorithm for color photographs. For image segmentation, if the pixel grid with eight neighbor connectivity is imposed as the graph, each group of pixels generated by this clustering method is ensured to be a geometrically connected region in the image, a desirable trait for many subsequent operations. To achieve scalability for images with large sizes, the segmentation algorithm combines the top‐down k ‐means clustering with the bottom‐up agglomerative clustering method. We also find that it is advantageous to conduct clustering at multiple stages through which the similarity measure is adjusted. Experimental results with comparison to other widely used and state‐of‐the‐art segmentation methods show that the new algorithm achieves higher accuracy at much faster speed. A software package is provided for public access. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 84–99 2011