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Abstract
Independent Component Analysis (ICA) is a powerful tool with applications in many areas of blind signal processing; however, its key assumption, i.e. the statistical independence of the source signals, can be somewhat restricting in some particular cases. For example, when considering several images, it is tempting to look on them as independent sources (the picture subjects are different), although they may actually be highly correlated (subjects are similar). Pictures of several landscapes (or faces) fall in this category. How to separate mixtures of such pictures? This paper proposes an ICA algorithm that can tackle this apparently paradoxical problem. Experiments with mixtures of real images demonstrate the soundness of the approach.
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Lee, J., Vrins, F., & Verleysen, M. (2006). Non-Orthogonal Support-Width ICA. Proceedings the 14th European Symposium on Artificial Neural Networks (ESANN 2006), p. 351-358. https://hdl.handle.net/2078.5/254168