Image representation based on superpixels has become indispensable for improving efficiency in Computer Vision systems. Object recognition, segmentation, depth estimation, and body model estimation are some important problems where superpixels can be applied. However, superpixels can influence the efficacy of the system in positive or negative manner, depending on how well they respect the object boundaries in the image. In this paper, we improve superpixel generation by extending a popular

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... rithm -Simple Linear Iterative Clustering (SLIC) -to consider minimum path costs between pixel and cluster centers rather than their direct distances. This creates a new Image Foresting Transform (IFT) operator that naturally defines superpixels as regions of strongly connected pixels by choice of the most suitable path-cost function for a given application. Nonsmooth connectivity functions are also explored in our IFT-SLIC approach leading to improved performance. Experimental results indicate better superpixel extraction using the proposed approach as compared to that of SLIC.