Embedded Integration Strategy to Image Segmentation Using Canny Edge and K-Means Algorithm
Keywords:
Image Segmentation, Canny Edge, K-means clusteringAbstract
Fusion based approaches give us ways to utilize information from one method to improve outcomes of other method. It has been found that the most of the traditional segmentation techniques can provide good segmentation results if additional information is provided to these segmentation strategies. K-means has the benefit of being a very straightforward
approach that produces good segmentation outcomes. The drawback of k-means approach is that the individual using it must predict the optimal count of clusters for the image because predicted count of clusters must be provided as a parameter. Varying results can be obtained by choosing different value k. An integrated segmentation technique have been presented in this paper that combines canny edge detection based edge processing and K-means image segmentation techniques for superior output. Canny edge detector is used to find edge maps. Edge maps were obtained by searching for local maxima and hysteresis thresholding. After discovering boundaries, long edge-lines were grouped and assigned same label for predicting approximate distribution of objects in image. The count of long edges remained after edge processing is employed as parameter k to k-means segmentation process. This count was used as a constraint on k to k-means algorithm. Experimentation shows that predicted value of k using proposed method yields good k-means segmentation outcomes.
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