Adaptive tuning of SLIC parameter K

The well-known simple linear iterative clustering (SLIC) is the most effective among the existing algorithms for superpixel segmentation, which requires manual tuning of the number of superpixels K. The optimal value of the parameter K of the SLIC algorithm for a given image is yet an open issue. In this work, we present granulometry and quality metrics based methods for adaptive tuning of the parameter K. The proposed granulometric method exploits the weighted average of the image pattern spectrum for the adaptive tuning of the parameter K. In the quality metrics method, we use majority voting scheme based on information, texture and ground truth independent quality metrics. The experimental results demonstrate that the K SLIC superpixels from the proposed methods achieved good boundary adherence of the ground truth for the images with high value of the compactness.