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--- research:visual-learning [2012/02/15 19:07]
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-===== Visual Learning =====
-A core interest lies in visual perception as part of closed-loop interactive tasks, and in particular, on systems that improve their performance with experience. Examples of our work include reinforcement learning within perception-action loops, image classification that drives machine learning to the extreme, and visuomotor learning for various purposes including object detection, recognition and manipulation.
-Reinforcement Learning on Visual Perception
-{{ :research:rlvc-tree.gif?200|Animated decision tree}}Using learning approaches on visual input is a challenge because of the high dimensionality of the raw pixel data. In this work, we bring introduce concepts from appearance-based computer vision to reinforcement learning. Our RLVC algorithm (Reinforcement Learning of Visual Classes, [[/publications#Jodogne-2007-JAIR|Jodogne & Piater JAIR 28:349–391, 2007]]) initially treats the visual input space as a single, perceptually aliased state, which is then iteratively split on local visual features, forming a decision tree. In this way, perceptual learning and policy learning are interleaved, and the system learns to focus its attention on relevant visual features.
-{{:research:joint-space.png?150 |Joint space}}Our RLJC algorithm (Reinforcement Learning of Joint Classes, [[/publications#Jodogne-2006-ECML-222|Jodogne & Piater ECML 2006]]), extends this idea to the combined perception-action space. This constitutes a promising new approach to the age-old problem of applying reinforcement learning to high-dimensional and/or continuous action spaces.
-==== Image Classification using Extra-Trees and Random Patches ====
-Image classification remains a difficult problem in general, and the best results on specific problems are usually obtained using specifically tailored methods.
-We came up with a generic method that turns this principle upside-down and nevertheless achieves highly competitive results on several, very different data sets ([[/publications#Maree-2005-CVPR|Marée, Geurts, Piater, Wehenkel CVPR 2005]]). It is based on three straightforward insights:
-    * Randomness to keep classifier bias down,
-    * Local patches to increase robustness to partial occlusions and global phenomena such as viewpoint changes,
-    * Normalization to achieve invariance to various transformations.
-The key contribution was probably the demonstration of how far randomization can take us: Local patches are extracted at random, rotational invariance is obtained by randomly rotating the training patches, and classification is done using Extremely Randomized Trees.

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