Proc. SPIE. 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
KEYWORDS: Image segmentation, Image processing algorithms and systems, Simulation of CCA and DLA aggregates, Evolutionary algorithms, Detection and tracking algorithms, Synthetic aperture radar, Optimization (mathematics), Image analysis, Data modeling, Edge detection
Image segmentation is the prerequisite step for further image analysis. Segmentation algorithms based on clustering
attract more and more attentions. In this paper, an image-domain based clustering method for segmentation, called CSA-CA,
is proposed. In this method, a scale parameter is introduced instead of an apriori known number of clusters.
Considering that adjacent pixels are generally not independent of each other, the spatial local context is took account into
our method. A spatial information term is added so that the near pixels have higher probability to merge into one cluster.
Additionally, a clonal selection clustering operator is used so that a cluster is capable of exploring the others that are not
neighboring in spatial but similar in feature. In the experiments we show the effectiveness of the proposed method and
compare it to other segmentation algorithms.