CO-SEGMENTATION OF MULTIPLE IMAGES THROUGH RANDOM WALK ON GRAPHS
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2016 IEEE. We present a new image co-segmentation framework to simultaneously segment multiple images by formulating the co-segmentation problem as a multiple graph clustering problem. For each image, we first construct a corresponding segment graph by extracting superpixels as vertices and assigning edge weights between superpixels according to their feature and spatial proximity. To integrate the related information across images, we further compose a similarity graph across all constructed segment graphs, in which edges capture the similarity among superpixels across images. We propose to solve the co-segmentation problem by applying an alternating random walk strategy on both the segment graphs and the similarity graph to borrow strengths across images for better segmentation. The common objects shared in images can be identified by finding low conductance sets based on the transition probability matrix of the alternating random walk on these graphs. Experiments on iCoseg and a sequence of echocardiac images demonstrate that our novel formulation yields promising results and performs better than image segmentation on individual images separately.
name of conference
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)