Xingping Dong

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A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for(More)
We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint,(More)
The online learning methods are popular for visual tracking because of their robust performance for most video sequences. However, the drifting problem caused by noisy updates is still a challenge for most highly adaptive online classifiers. In visual tracking, target object appearance variation, such as deformation and long-term occlusion, easily causes(More)
In this paper, we present a supervoxel generation algorithm based on partially absorbing random walks to get more accurate supervoxels in these regions. A novel spatial-temporal framework is introduced by making full use of the appearance features and motion cues, which effectively exploits the temporal consistency in the video sequence. Moreover, we build(More)
A novel energy minimization method for general higher order binary energy functions is proposed in this paper. We first relax a discrete higher order function to a continuous one, and use the Taylor expansion to obtain an approximate lower order function, which is optimized by the quadratic pseudo-Boolean optimization or other discrete optimizers. The(More)
As a discriminative method of one-shot learning, Siamese deep network allows recognizing an object from a single exemplar with the same class label. However, it does not take the advantage of the underlying structure and relationship among a multitude of instances since it only relies on pairs of instances for training. In this paper, we propose a(More)
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