Lazy Random Walks for Superpixel Segmentation


We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.

DOI: 10.1109/TIP.2014.2302892

Extracted Key Phrases

9 Figures and Tables

Citations per Year

62 Citations

Semantic Scholar estimates that this publication has 62 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Shen2014LazyRW, title={Lazy Random Walks for Superpixel Segmentation}, author={Jianbing Shen and Yunfan Du and Wenguan Wang and Xuelong Li}, journal={IEEE Transactions on Image Processing}, year={2014}, volume={23}, pages={1451-1462} }