Francis Bach, Miki Elad and myself are editing a special issue on sparse coding of the international journal of computer vision (IJCV), which might be of potential interest for the readers of your blog. We are welcoming contributions developing novel sparse coding techniques for computer vision and image processing problems, novel applications of sparse coding, as well as theoretical contributions that are relevant to computer vision.The submission deadline will be January 31st, 2014.For more information, the call for papers is available here:
Aims and ScopeSparse models have gained a tremendous success during the last years in various scientiﬁc ﬁelds. In statistics and machine learning, the sparsity principle is used to perform model selection—that is, selecting a simple model among a large collection of them. This is interpreted as automatically selecting a few predictors that explain the observed data. In signal processing, sparsity is used for approximating signals as a linear combination of a few dictionary elements, imposing a union-of-subspaces model on the true data. Not surprisingly, similar formulations and algorithms have been developed in statistics and signal processing, from different point of views, and are nowextremely popular in both disciplines.The image processing and computer vision communities are a dominant part of this trend, and we have seen a growing interest in sparse models and their deployment to applications in these ﬁelds. In particular,methods where the dictionary is learned from data have been successfully used for a wide range of computer vision and image processing tasks, such as feature and codebook learning, image restoration, super-resolution,compression, visual tracking, and many others. This special issue of IJCV welcomes submissions developing novel sparse coding techniques for computer vision and image processing problems, novel applications of sparse coding, as well as theoretical contributions that are relevant to computer vision.