FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

Non-linear generative models and their applications
Collaborators: J. Lücke 1 , M. Henniges 1 , A. S. Sheikh 1 , J. Bornschein 1 , Z. Dai 1 , J. Eggert 2 , R. Turner 3 , M.
Sahani 4 , Marc-Thilo Figge 5
1 Frankfurt Institute for Advanced Studies, 2 Honda Research Institute, Europe, 3 Dept of Engineering, University of Cambridge,
UK, 4 Gatsby Computational Neuroscience Unit, University College London, UK, 5 Applied Systems Biology,
HKI, Friedrich-Schiller-Universität Jena, Germany
The salient components of visual scenes are objects. While the visual system of humans and animals identifies
visual objects with apparent ease, this task, in general, is still considered as one of the major unsolved problems
in computer vision. Learning from visual data is challenging because visual data components combine nonlinearly,
are dependent, and their identities remain unchanged by position changes. In contrast, state-of-the-art
approaches such as independent component analysis (ICA) assume linear component combination, independent
components, and fixed component positions. In the DFG project “Non-linear Generative Models for Representational
Recognition and Unsupervised Learning in Vision” we develop a new theoretical framework that allows
for the development of approaches that go beyond the state-of-the-art in all these aspects. Based on the newly
developed theoretical tools (Lücke, Eggert, 2010; Shelton et al., 2011; Lücke, Henniges, 2012) we developed
algorithms reflecting the dicreteness of objects (Exarchakis et al., 2012; Lücke, Sheikh, 2012) and algorithms
with explicit encoding of object location (Dai, Lücke, 2012). Furthermore, we continued the development and
application of algorithms with non-linear component superposition (Lücke, Sahani, 2008; Lücke et al., 2009;
Puertas et al., 2010) in the Bernstein Focus Neurotechnology Frankfurt.
In applications of the new algorithms to visual data, we could show that explicit representations of visual patterns
can be learned autonomously. We have shown that representations can be learned from data with occluding
objects, have demonstrated applicability to data from scanned text documents, and have started a collaboration
with the Applied Systems Biology at Jena Univerity (Marc-Thilo Figge) to learn cell representations from
microscopy images for image understanding in systems immunology (see Figure).
Left: Inferred objects after learning. Right: Inferred cell positions and types on microscopy images from immunological
processes.
Related publications in 2011:
Lücke, J., and Sheikh, A. S. (2011). A Closed-Form EM Algorithm for Sparse Coding and Its Application to
Source Separation (http://arxiv.org/abs/1105.2493).
Lücke, J. and Henniges, M. (2012). Closed-Form Entropy Limits – A Tool to Monitor Likelihood Optimization
of Probabilistic Generative Models. Proceedings AISTATS, in press, accepted 2011.
Exarchakis, G., Henniges, M., Eggert, J., and Lücke, J. (2012). Ternary Sparse Coding. Proceedings ICA/LVA,
LNCS 7191, 204–212, accepted 2011.
Dai, Z., and Lücke, J. (2012). Unsupervised Learning of Translation Invariant Occlusive Components. IEEE
Conference on Computer Vision and Pattern Recognition, submitted 2011, accepted.
Dai, Z., and Lücke, J. (2012). Autonomous Cleaning of Corrupted Scanned Documents – A Generative Modeling
Approach. IEEE Conference on Computer Vision and Pattern Recognition, accepted for oral presentation.
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