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→Discriminative Dictionary Learning for Sparse Coding : A Batch Version and a Semi-Supervised Online Version
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
In the second part of the talk, we present an online, semi-supervised dictionary learning algorithm that is suitable when the size of the dataset is large and the labels are expensive to obtain. Similar to the previous work, our goal is to obtain a dictionary which is both representative and discriminative. Besides learning from labeled data, we also exploit the large amount of cheap, unlabeled training data to reinforce the representation power. An online framework makes the algorithm applicable to large-scale dataset.
===Boosted Regression Tree and its Application to Computer Vision===
Speaker: [http://www.kotahara.com/ Kota Hara] -- Date: February 28, 2013
Boosting techniques have been applied to various computer vision tasks, however, most of them are used for classification purposes. In this talk, I will first present Boosted Regression Tree (BRT), a regression version of the boosting used with regression trees, and its simple extension to multidimensional output regression. Then I will show the applications of the BRT on three computer vision tasks, head pose estimation, human pose estimation and class-specific object shape estimation. The BRT is applied to the head pose estimation task straightforwardly. In the human pose estimation task, a body pose estimation task is divided into a set of local pose estimation tasks that maintain a dependency structure and the BRT is used to solve those local pose estimation tasks in a successive manner. Lastly, the class-specific object shape estimation task is addressed by introducing a low dimensional space representing the object shape manifold and using it to bridge the image feature space and the original object shape space.
===Statistical Multi-Scale Decomposition Modeling of Texture and Applications===
===Statistical Multi-Scale Decomposition Modeling of Texture and Applications===
