Abstract
In this paper, a temporal superpixel algorithm using proximity-weighted patch matching (PPM) is proposed to yield temporally consistent superpixels for image and video processing. PPM estimates the motion vector of a superpixel robustly, by considering the patch matching distances of neighboring superpixels as well as the superpixel itself. In each frame, we initialize superpixels by transferring the superpixel labels of the previous frame using PPM motion vectors. Then, we update the superpixel labels of boundary pixels by minimizing a cost function, which is composed of feature distance, compactness, contour, and temporal consistency terms. Finally, we carry out superpixel splitting, merging, and relabeling to regularize superpixel sizes and correct inaccurate labels. Extensive experimental results confirm that the proposed algorithm outperforms the state-of-the-art conventional algorithms significantly. Also, it is demonstrated that the proposed algorithm can be applied to video object segmentation and video saliency detection tasks.
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