An improved adaptive spectral clustering for image segmentation / 南方医科大学学报

ABSTRACT

<p><b>OBJECTIVE</b>To propose an improved adaptive spectral clustering method for image segmentation to allow automatic selection of the optimal scaling parameters and enhance the accuracy of spectral clustering.</p><p><b>METHODS</b>Using constrain conditions for optimizing the criterion function and determining the optimal scaling parameters by iteration, the final image segmentation was achieved through spectral clustering based on Nystrom approximation. We chose suit weight functions for different texture images, and used the proposed method for image segmentation. The k-means algorithm and the method of spectral clustering after pre-segmentation by manually choosing the scaling parameter were compared with the proposed method.</p><p><b>RESULTS</b>The improved spectral clustering algorithm with automatic selection of the optimal scaling parameters achieved better results of image segmentation than the other two methods.</p><p><b>CONCLUSION</b>The proposed algorithm can improve the accuracy of spectral clustering for image segmentation.</p>