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2012 | OriginalPaper | Chapter
Scalable Spectral Clustering Combined with Adjacencies Merging for Image Segmentation
Authors : Li You, Shilin Zhou, Gui Gao, Meng Leng
Published in: Advances in Computer, Communication, Control and Automation
Publisher: Springer Berlin Heidelberg
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A novel scalable graph partitioning framework has been proposed in this paper. The scalable graph partitioning is new thought to deal with the large scale images, which improves the efficiency greatly and maintains the major local details. It involves two levels clustering, namely blockwise and segment, to achieve the excellent performance. In this paper, spectral clustering has been implemented twice combined with the morphologic adjacencies separating and merging algorithm to obtain the final segmentation results. Experimental results show that it keeps fine details and removes the noise pixels generated by spectral clustering.