We propose a novel stochastic graph matching algorithm based on data-driven Markov Chain Monte Carlo (DDMCMC) sampling technique. The algorithm explores the solution space efficien...
Geometric methods are very intuitive and provide a theoretically solid viewpoint to many optimization problems. SVM is a typical optimization task that has attracted a lot of atte...
Michael E. Mavroforakis, Margaritis Sdralis, Sergi...
A novel stochastic searching scheme based on the Monte Carlo optimization is presented for polygonal approximation (PA) problem. We propose to combine the split-and-merge based lo...
This paper introduces a clever way of computing inner products between images in order to drastically reduce the computational complexity of fitting appearance models to images. T...
This paper addresses the limitation of current multilinear PCA based techniques, in terms of prohibitive computational cost of testing and poor generalisation in some scenarios, w...
Santu Rana, Wanquan Liu, Mihai Lazarescu, Svetha V...