Workshop on Image Processing with Machine Learning
With the increasing attention in recent years, of interest to this special issue is research that demonstrates how machine learning algorithms have contributed, and are contributing to the research and applications of intelligent image processing. It is not difficult to enumerate a large number of successful examples of using machine learning in intelligent image processing, e.g., structured sparsity has been successfully applied to image and video modeling; human machine interactions significantly improve the performance of large scale image retrieval; sparse linear and multilinear subspace methods dramatically enhance the recognition rates in human behavior analysis and face synthesis; random fields and probabilistic graphical models show promising advantages in image and video analysis; graph cut and spectral clustering are widely applied to image segmentation; kernel machines, such as the support vector machines, are successfully used in visual tracking and handwriting recognition; and reinforcement learning is applied to visual texture synthesis.
It is the time to motivate image processing researchers and machine learning researchers to work together and pay more attention to each other’s field. Therefore, there is a chance to obtain significant performance improvement for practical utilizations of intelligent image processing by developing particular learning algorithms, and to bring in interesting utilizations of machine learning algorithms for particular intelligent image processing. The editors expect to gather a set of recent research outputs together, to report the progress of what is going on, and to build a forum for researchers to exchange their innovative ideas on machine learning in intelligent image processing.
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